Pub Date : 2025-11-26DOI: 10.1177/00220345251387660
J.J. Wong, O. Urquhart, A. Carrasco-Labra, E.F. Schisterman, M. Glick
Administrative health care data offer unique opportunities to investigate relationships between oral and systemic diseases. However, these data sources introduce methodological challenges that can compromise causal inference. This article demonstrates how, in the context of claims databases, selection bias (i.e., arising from restricting analyses to individuals with both dental and medical insurance) creates a collider structure that can distort estimates of periodontal treatment effects on systemic disease outcomes. Drawing on causal inference theory, we distinguish between confounding (resulting from common causes) and selection bias (resulting from common effects) and demonstrate how directed acyclic graphs (DAGs) can identify these biases and inform rigorous analytical strategies. Therefore, the goal of this article is to demonstrate how selection and confounding biases in administrative health care claims data can compromise causal inference in periodontal–systemic disease research and to introduce methodological approaches for addressing these threats. Our review of 7 studies investigating periodontal–systemic disease associations using claims data reveals methodological gaps in addressing selection bias in the current literature. Moreover, through a numerical example, we illustrate how selection bias can not only distort but also potentially reverse observed associations, producing contradictory clinical recommendations. To address these methodological threats, we introduce established causal inference strategies, referencing implementation tutorials: for confounding, we reference G-methods (G-formula, inverse probability weighting) and stratification-based approaches (regression, matching); for selection bias, we reference inverse probability of selection weighting approaches when data on nonselected individuals are available. To improve methodological rigor in oral–systemic research, we advocate for (1) routine use of DAGs with freely available software, (2) application of bias-correction techniques using established statistical packages, and (3) transparent reporting of bias assessment procedures. Strengthening causal inference methodology in dental research is paramount to building a robust evidence base on periodontal–systemic relationships that supports clinical decision making and integration of oral health into broader health care frameworks.
{"title":"Addressing Selection and Confounding Biases in Dental Claims Data: A Causal Inference Framework for Periodontal–Systemic Disease Research","authors":"J.J. Wong, O. Urquhart, A. Carrasco-Labra, E.F. Schisterman, M. Glick","doi":"10.1177/00220345251387660","DOIUrl":"https://doi.org/10.1177/00220345251387660","url":null,"abstract":"Administrative health care data offer unique opportunities to investigate relationships between oral and systemic diseases. However, these data sources introduce methodological challenges that can compromise causal inference. This article demonstrates how, in the context of claims databases, selection bias (i.e., arising from restricting analyses to individuals with both dental and medical insurance) creates a collider structure that can distort estimates of periodontal treatment effects on systemic disease outcomes. Drawing on causal inference theory, we distinguish between confounding (resulting from common causes) and selection bias (resulting from common effects) and demonstrate how directed acyclic graphs (DAGs) can identify these biases and inform rigorous analytical strategies. Therefore, the goal of this article is to demonstrate how selection and confounding biases in administrative health care claims data can compromise causal inference in periodontal–systemic disease research and to introduce methodological approaches for addressing these threats. Our review of 7 studies investigating periodontal–systemic disease associations using claims data reveals methodological gaps in addressing selection bias in the current literature. Moreover, through a numerical example, we illustrate how selection bias can not only distort but also potentially reverse observed associations, producing contradictory clinical recommendations. To address these methodological threats, we introduce established causal inference strategies, referencing implementation tutorials: for confounding, we reference G-methods (G-formula, inverse probability weighting) and stratification-based approaches (regression, matching); for selection bias, we reference inverse probability of selection weighting approaches when data on nonselected individuals are available. To improve methodological rigor in oral–systemic research, we advocate for (1) routine use of DAGs with freely available software, (2) application of bias-correction techniques using established statistical packages, and (3) transparent reporting of bias assessment procedures. Strengthening causal inference methodology in dental research is paramount to building a robust evidence base on periodontal–systemic relationships that supports clinical decision making and integration of oral health into broader health care frameworks.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"27 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-26DOI: 10.1177/00220345251388340
D.T. Graves, M.A. Levine, S. Aldosary, R.T. Demmer
Diabetes mellitus (DM) and periodontitis share a complex, bidirectional relationship, with each condition exacerbating the other. Diabetes, particularly when poorly controlled, significantly increases the risk, severity, and progression of periodontitis. The biological mechanisms involved are complex and numerous. Hyperglycemia in diabetes is linked to oral microbial dysbiosis, which is in turn associated with increased inflammation, epithelial barrier dysfunction, impaired neutrophil and macrophage function, altered T-cell profiles, and cytokine imbalance, collectively fostering chronic inflammation and immune dysregulation. Moreover, diabetes alters bone metabolism, promoting osteoclastogenesis and reducing reparative bone regeneration by limiting coupled bone formation through an effect on growth factor production, mesenchymal stems cells, and osteoblasts. Conversely, periodontitis is strongly linked to poor glycemic control. Clinical studies and longitudinal meta-analyses report consistent positive associations, while randomized controlled trials show that periodontal therapy reduces HbA1c by ~0.43%. Emerging evidence suggests that periodontitis and oral preclinical dysbiosis contribute to diabetogenesis, although causality remains uncertain. Periodontitis may drive metabolic dysfunction through several biological mechanisms. The dysbiotic oral microbiome and subsequent periodontitis may promote systemic inflammation and subsequent insulin resistance and glucose intolerance. Moreover, oral dysbiosis may deplete nitrate-reducing taxa and impair nitric oxide pathways, which has relevance to both periodontal and cardiometabolic health. Accordingly, periodontal treatment in diabetic populations has shown potential health care savings. Nevertheless, trials assessing the influence of periodontitis treatment on systemic outcomes consistently show significant treatment heterogeneity, which requires explication in future studies. This review underscores the systemic implications of periodontitis in diabetes and highlights the value of integrating periodontal care into diabetes management. A better understanding of the shared pathophysiology between these diseases supports interdisciplinary approaches and points toward novel preventive and therapeutic strategies targeting inflammation, microbial balance, and host response modulation to jointly benefit periodontal and cardiometabolic health.
{"title":"Understanding the Periodontitis–Diabetes Linkage: Mechanisms and Evidence","authors":"D.T. Graves, M.A. Levine, S. Aldosary, R.T. Demmer","doi":"10.1177/00220345251388340","DOIUrl":"https://doi.org/10.1177/00220345251388340","url":null,"abstract":"Diabetes mellitus (DM) and periodontitis share a complex, bidirectional relationship, with each condition exacerbating the other. Diabetes, particularly when poorly controlled, significantly increases the risk, severity, and progression of periodontitis. The biological mechanisms involved are complex and numerous. Hyperglycemia in diabetes is linked to oral microbial dysbiosis, which is in turn associated with increased inflammation, epithelial barrier dysfunction, impaired neutrophil and macrophage function, altered T-cell profiles, and cytokine imbalance, collectively fostering chronic inflammation and immune dysregulation. Moreover, diabetes alters bone metabolism, promoting osteoclastogenesis and reducing reparative bone regeneration by limiting coupled bone formation through an effect on growth factor production, mesenchymal stems cells, and osteoblasts. Conversely, periodontitis is strongly linked to poor glycemic control. Clinical studies and longitudinal meta-analyses report consistent positive associations, while randomized controlled trials show that periodontal therapy reduces HbA1c by ~0.43%. Emerging evidence suggests that periodontitis and oral preclinical dysbiosis contribute to diabetogenesis, although causality remains uncertain. Periodontitis may drive metabolic dysfunction through several biological mechanisms. The dysbiotic oral microbiome and subsequent periodontitis may promote systemic inflammation and subsequent insulin resistance and glucose intolerance. Moreover, oral dysbiosis may deplete nitrate-reducing taxa and impair nitric oxide pathways, which has relevance to both periodontal and cardiometabolic health. Accordingly, periodontal treatment in diabetic populations has shown potential health care savings. Nevertheless, trials assessing the influence of periodontitis treatment on systemic outcomes consistently show significant treatment heterogeneity, which requires explication in future studies. This review underscores the systemic implications of periodontitis in diabetes and highlights the value of integrating periodontal care into diabetes management. A better understanding of the shared pathophysiology between these diseases supports interdisciplinary approaches and points toward novel preventive and therapeutic strategies targeting inflammation, microbial balance, and host response modulation to jointly benefit periodontal and cardiometabolic health.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"3 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1177/00220345251383863
J S Patel,E Dinh
Despite well-established connections between oral and systemic health, electronic health records (EHRs) and electronic dental records (EDRs) remain largely siloed due to infrastructural and interoperability challenges. This separation limits interdisciplinary care and data-driven research to generate practice-based evidence. We developed and validated 4 algorithmic frameworks specifically designed to link EHR with EDR across nonintegrated systems. Using data from more than 1.7 million medical records and 222,480 dental records spanning a 10-y period at Temple University, we evaluated 4 linkage strategies: (1) direct Social Security number matching, (2) unweighted similarity scoring, (3) weighted average similarity scoring, and (4) a probabilistic expectation-conditional maximization classification model. We compared these approaches using expert-reviewed validation of 1,000 candidate record pairs and selected optimal similarity thresholds for high-fidelity linkages. Our weighted average similarity algorithm demonstrated the best performance with 100% specificity (correctly avoiding false matches), 99% sensitivity (correctly identifying all true matches), and 99% accuracy (proportion of all correct linkages out of total comparisons) at the threshold of 0.82 for successfully linking 121,771 unique patients and 144,229 patients' linkage with 96% sensitivity, 78% specificity, and 89% accuracy. After linking the datasets, the completeness of key patient demographic information significantly improved, with missing race data reduced from 79% to 11% and missing ethnicity data from 82% to 17%. We designed the algorithm to be transparent and vendor neutral, making it potentially adaptable to any institution or practice regardless of their existing EHR/EDR systems. This provides a foundation for developing a clinical decision support systems that facilitate real-time health information exchange, supporting safer dental procedures, timely medical referrals, and integrative research. Our findings provide a critical bridge between medicine and dentistry, which have remained largely divorced from each other. Future work will focus on multi-institutional validation, implementation, and integration into routine clinical workflows.
{"title":"LinkMD: Linking Medical and Dental Records with 4 Linking Algorithms.","authors":"J S Patel,E Dinh","doi":"10.1177/00220345251383863","DOIUrl":"https://doi.org/10.1177/00220345251383863","url":null,"abstract":"Despite well-established connections between oral and systemic health, electronic health records (EHRs) and electronic dental records (EDRs) remain largely siloed due to infrastructural and interoperability challenges. This separation limits interdisciplinary care and data-driven research to generate practice-based evidence. We developed and validated 4 algorithmic frameworks specifically designed to link EHR with EDR across nonintegrated systems. Using data from more than 1.7 million medical records and 222,480 dental records spanning a 10-y period at Temple University, we evaluated 4 linkage strategies: (1) direct Social Security number matching, (2) unweighted similarity scoring, (3) weighted average similarity scoring, and (4) a probabilistic expectation-conditional maximization classification model. We compared these approaches using expert-reviewed validation of 1,000 candidate record pairs and selected optimal similarity thresholds for high-fidelity linkages. Our weighted average similarity algorithm demonstrated the best performance with 100% specificity (correctly avoiding false matches), 99% sensitivity (correctly identifying all true matches), and 99% accuracy (proportion of all correct linkages out of total comparisons) at the threshold of 0.82 for successfully linking 121,771 unique patients and 144,229 patients' linkage with 96% sensitivity, 78% specificity, and 89% accuracy. After linking the datasets, the completeness of key patient demographic information significantly improved, with missing race data reduced from 79% to 11% and missing ethnicity data from 82% to 17%. We designed the algorithm to be transparent and vendor neutral, making it potentially adaptable to any institution or practice regardless of their existing EHR/EDR systems. This provides a foundation for developing a clinical decision support systems that facilitate real-time health information exchange, supporting safer dental procedures, timely medical referrals, and integrative research. Our findings provide a critical bridge between medicine and dentistry, which have remained largely divorced from each other. Future work will focus on multi-institutional validation, implementation, and integration into routine clinical workflows.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"7 1","pages":"220345251383863"},"PeriodicalIF":7.6,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1177/00220345251385966
S Warnakulasuriya,P Ramos-García,M Á González-Moles
The mouth is referred to as "the mirror of health and disease in the body." This review critically examines the comorbidity between systemic diseases and oral lichen planus, an autoimmune disorder affecting the oral mucosa with malignant potential and of high worldwide prevalence. Research has indicated that patients with oral lichen planus are significantly predisposed to diabetes mellitus (pooled proportion [PP] = 9.77%, odds ratio [OR] = 1.64, P < 0.001), Hashimoto thyroiditis (PP = 8.60%, OR = 2.2, P < 0.001), hypothyroidism (PP = 8.14%, OR = 1.65, P = 0.02), hyperthyroidism (PP = 2.84%, OR = 2.11, P = 0.007), celiac disease (PP = 7.14%, OR = 4.09, P < 0.001), hepatitis C (PP = 7.14%, OR = 4.09, P < 0.001), hepatitis B (PP = 3.90%, OR = 1.62, P = 0.02), steatohepatitis (PP = 7.06%, OR = 5.71, P = 0.05), liver cirrhosis (PP = 4.27%, OR = 5.8, P = 0.002), depression (PP = 31.19%, OR = 6.15, P < 0.001), anxiety (PP = 54.76%, OR = 3.51, P < 0.001), and stress (PP = 41.10%, OR = 3.64, P = 0.005). A good knowledge of these associations may assist primary care physicians, dentists, and other oral health professionals involved in the management of patients with oral lichen planus since many patients may be unaware of these associations and could have an impact on their general health. Some of these diseases, such as diabetes, have a role in the development of oral lichen planus. In addition, most of these comorbidities act as risk factors for cancer of different locations: liver, thyroid, small intestine, and the oral cavity. Current evidence indicates a high prevalence and a higher risk of systemic diseases in patients with oral lichen planus compared with the general population. Future research is recommended to increase our knowledge of pathobiology and clinical management of these associations.
{"title":"Oral Lichen Planus and Systemic Diseases.","authors":"S Warnakulasuriya,P Ramos-García,M Á González-Moles","doi":"10.1177/00220345251385966","DOIUrl":"https://doi.org/10.1177/00220345251385966","url":null,"abstract":"The mouth is referred to as \"the mirror of health and disease in the body.\" This review critically examines the comorbidity between systemic diseases and oral lichen planus, an autoimmune disorder affecting the oral mucosa with malignant potential and of high worldwide prevalence. Research has indicated that patients with oral lichen planus are significantly predisposed to diabetes mellitus (pooled proportion [PP] = 9.77%, odds ratio [OR] = 1.64, P < 0.001), Hashimoto thyroiditis (PP = 8.60%, OR = 2.2, P < 0.001), hypothyroidism (PP = 8.14%, OR = 1.65, P = 0.02), hyperthyroidism (PP = 2.84%, OR = 2.11, P = 0.007), celiac disease (PP = 7.14%, OR = 4.09, P < 0.001), hepatitis C (PP = 7.14%, OR = 4.09, P < 0.001), hepatitis B (PP = 3.90%, OR = 1.62, P = 0.02), steatohepatitis (PP = 7.06%, OR = 5.71, P = 0.05), liver cirrhosis (PP = 4.27%, OR = 5.8, P = 0.002), depression (PP = 31.19%, OR = 6.15, P < 0.001), anxiety (PP = 54.76%, OR = 3.51, P < 0.001), and stress (PP = 41.10%, OR = 3.64, P = 0.005). A good knowledge of these associations may assist primary care physicians, dentists, and other oral health professionals involved in the management of patients with oral lichen planus since many patients may be unaware of these associations and could have an impact on their general health. Some of these diseases, such as diabetes, have a role in the development of oral lichen planus. In addition, most of these comorbidities act as risk factors for cancer of different locations: liver, thyroid, small intestine, and the oral cavity. Current evidence indicates a high prevalence and a higher risk of systemic diseases in patients with oral lichen planus compared with the general population. Future research is recommended to increase our knowledge of pathobiology and clinical management of these associations.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"138 1","pages":"220345251385966"},"PeriodicalIF":7.6,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1177/00220345251368276
C. Neurath, H. Limeback, C.V. Howard
{"title":"Letter to the Editor, “Early Childhood Exposures to Fluorides and Cognitive Neurodevelopment: A Population-Based Longitudinal Study”","authors":"C. Neurath, H. Limeback, C.V. Howard","doi":"10.1177/00220345251368276","DOIUrl":"https://doi.org/10.1177/00220345251368276","url":null,"abstract":"","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"33 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1177/00220345251382452
R. O’Kane, D. Stonehouse-Smith, L.C.U. Ota, R. Patel, N. Johnson, C. Slipper, J. Seehra, S.N. Papageorgiou, M.T. Cobourne
Accurate clinical records are fundamental to dental practice. Automatic speech recognition (ASR) has the capacity to convert spoken clinical language into written text within the electronic health record; however, the accuracy of ASR in natural language processing for clinical dentistry remains uncertain. The aim of this study was to investigate the transcriptional accuracy of ASR systems using orthodontic clinical records as the experimental model. Specifically, we used 4 commercial ASR systems (Heidi Health, DigitalTCO, Dragon Medical One, Dragon Professional Anywhere), 5 application programming interfaces (Amazon, Google, Speechmatics, Whisper, GPT4oTranscribe), and a 2-stage pipeline coupling GPT4oTranscribe with the GPT4o large language model (LLM) for generative error correction (GPT4oTranscribeCorrected). Orthodontic diagnostic and treatment planning summaries ( <jats:italic toggle="yes">n</jats:italic> = 200; 10 subject domains; 43,408 words; 6 h of audio) were narrated and recorded for analysis. The primary outcome was domain word error rate (DWER), which investigates clinical terminological transcription errors against the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) database. Secondary outcomes included nondomain WER (N-DWER), lexical accuracy (Recall-Oriented Understudy for Gisting Evaluation [ROUGE] score), semantic similarity (Bidirectional Encoder Representations from Transformers [BERT] and Bidirectional and Auto-Regressive Transformer [BART] scores), hallucinations (transcribed text not in the spoken input), and qualitative error analysis. GPT4oTranscribeCorrected was transcriptionally most accurate (DWER = 3.5%; WER = 3.7%), with DWER decreasing by 54.9% versus GPT4oTranscribe. Heidi Health was the highest-performing commercial system (DWER = 6.2%; WER = 5.4%), with Dragon Professional Anywhere being the worst (WER = 33.9%). All systems were less accurate with technical vocabulary (DWER > N-DWER; <jats:italic toggle="yes">P</jats:italic> < 0.001), except GPT4oTranscribeCorrected. Significant differences were seen across systems for ROUGE, BERT, and BART scores ( <jats:italic toggle="yes">P</jats:italic> < 0.001). Based on post hoc pairwise comparisons, GPT4oTranscribeCorrected performed best and Dragon Professional Anywhere was consistently worst for lexical and semantic errors. Hallucinations were absent except for Whisper ( <jats:italic toggle="yes">n</jats:italic> = 57) and DigitalTCO ( <jats:italic toggle="yes">n</jats:italic> = 1). Across systems, background noise increased DWER and WER ( <jats:italic toggle="yes">P</jats:italic> < 0.001). Importantly, clinically significant errors were seen with all systems, ranging from 2% to 66% (GPT4oTranscribeCorrected clean; Dragon Medical One background noise, respectively). Variation in narrator accent had no effect in clean conditions ( <jats:italic toggle="yes">P</jats:italic> = 0.65) and a small effect with background noise ( <jats:italic toggle="y
准确的临床记录是牙科实践的基础。自动语音识别（ASR）能够在电子健康记录中将口头临床语言转换为书面文本；然而，在临床牙科的自然语言处理中，ASR的准确性仍然不确定。本研究的目的是利用正畸临床记录作为实验模型来研究ASR系统的转录准确性。具体来说，我们使用了4个商业ASR系统（Heidi Health, DigitalTCO, Dragon Medical One, Dragon Professional Anywhere）， 5个应用程序编程接口（Amazon， b谷歌，Speechmatics, Whisper, gpt40transcripte），以及一个2级管道耦合gpt40transcripte与gpt40large language model (LLM)，用于生成纠错（gpt40transcribeccorrected）。对正畸诊疗计划总结（n = 200, 10个学科领域，43408个单词，6小时音频）进行叙述和记录以供分析。主要结果是领域词错误率（DWER），调查临床术语转录错误对医学临床术语系统化命名法（SNOMED-CT）数据库。次要结果包括非领域WER （N-DWER）、词汇准确性（面向记忆的替代评价评分[ROUGE]）、语义相似性（来自变形金刚的双向编码器表征[BERT]和双向和自回归变形金刚[BART]评分）、幻觉（语音输入中没有转录的文本）和定性错误分析。gpt40transcribeccorrected在转录上最准确（DWER = 3.5%; WER = 3.7%），与gpt40transcribe相比，DWER降低了54.9%。Heidi Health是表现最好的商业系统（WER = 6.2%; WER = 5.4%）， Dragon Professional Anywhere表现最差（WER = 33.9%）。所有系统在技术词汇方面的准确性都较低（DWER > N-DWER; P < 0.001），除了gpt40transcribe corrected。ROUGE、BERT和BART评分在不同系统之间存在显著差异（P < 0.001）。基于事后两两比较，gpt40transcribeccorrected表现最好，而Dragon Professional Anywhere在词汇和语义错误方面一直表现最差。除Whisper （n = 57）和DigitalTCO （n = 1）外，无幻觉。在各个系统中，背景噪声增加了DWER和WER （P < 0.001）。重要的是，所有系统的临床显著误差都在2%至66%之间（分别为gpt40、transcribeccorrected clean和Dragon Medical One背景噪声）。叙述者口音的变化在清洁条件下没有影响（P = 0.65），背景噪音的影响很小（P = 0.001）。ASR系统提供个位数的转录错误率，特别是当结合基于llm的校正时，但临床显著的错误仍然存在。当使用当前的ASR系统时，临床记录的验证是必不可少的。
{"title":"Transcription Accuracy of Automatic Speech Recognition for Orthodontic Clinical Records","authors":"R. O’Kane, D. Stonehouse-Smith, L.C.U. Ota, R. Patel, N. Johnson, C. Slipper, J. Seehra, S.N. Papageorgiou, M.T. Cobourne","doi":"10.1177/00220345251382452","DOIUrl":"https://doi.org/10.1177/00220345251382452","url":null,"abstract":"Accurate clinical records are fundamental to dental practice. Automatic speech recognition (ASR) has the capacity to convert spoken clinical language into written text within the electronic health record; however, the accuracy of ASR in natural language processing for clinical dentistry remains uncertain. The aim of this study was to investigate the transcriptional accuracy of ASR systems using orthodontic clinical records as the experimental model. Specifically, we used 4 commercial ASR systems (Heidi Health, DigitalTCO, Dragon Medical One, Dragon Professional Anywhere), 5 application programming interfaces (Amazon, Google, Speechmatics, Whisper, GPT4oTranscribe), and a 2-stage pipeline coupling GPT4oTranscribe with the GPT4o large language model (LLM) for generative error correction (GPT4oTranscribeCorrected). Orthodontic diagnostic and treatment planning summaries ( <jats:italic toggle=\"yes\">n</jats:italic> = 200; 10 subject domains; 43,408 words; 6 h of audio) were narrated and recorded for analysis. The primary outcome was domain word error rate (DWER), which investigates clinical terminological transcription errors against the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) database. Secondary outcomes included nondomain WER (N-DWER), lexical accuracy (Recall-Oriented Understudy for Gisting Evaluation [ROUGE] score), semantic similarity (Bidirectional Encoder Representations from Transformers [BERT] and Bidirectional and Auto-Regressive Transformer [BART] scores), hallucinations (transcribed text not in the spoken input), and qualitative error analysis. GPT4oTranscribeCorrected was transcriptionally most accurate (DWER = 3.5%; WER = 3.7%), with DWER decreasing by 54.9% versus GPT4oTranscribe. Heidi Health was the highest-performing commercial system (DWER = 6.2%; WER = 5.4%), with Dragon Professional Anywhere being the worst (WER = 33.9%). All systems were less accurate with technical vocabulary (DWER > N-DWER; <jats:italic toggle=\"yes\">P</jats:italic> < 0.001), except GPT4oTranscribeCorrected. Significant differences were seen across systems for ROUGE, BERT, and BART scores ( <jats:italic toggle=\"yes\">P</jats:italic> < 0.001). Based on post hoc pairwise comparisons, GPT4oTranscribeCorrected performed best and Dragon Professional Anywhere was consistently worst for lexical and semantic errors. Hallucinations were absent except for Whisper ( <jats:italic toggle=\"yes\">n</jats:italic> = 57) and DigitalTCO ( <jats:italic toggle=\"yes\">n</jats:italic> = 1). Across systems, background noise increased DWER and WER ( <jats:italic toggle=\"yes\">P</jats:italic> < 0.001). Importantly, clinically significant errors were seen with all systems, ranging from 2% to 66% (GPT4oTranscribeCorrected clean; Dragon Medical One background noise, respectively). Variation in narrator accent had no effect in clean conditions ( <jats:italic toggle=\"yes\">P</jats:italic> = 0.65) and a small effect with background noise ( <jats:italic toggle=\"y","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"12 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1177/00220345251379776
R. Tsuboi, Z. Zhang, K. Warner, S. The, E.T. Keller, J.E. Nör
Dental pulp stem cells (DPSCs) are neural crest–derived stem cells endowed with multipotency and self-renewal. While processes orchestrating DPSC differentiation have been studied extensively, mechanisms underpinning the differentiation of human DPSCs in vivo remain unclear. Here, we induced vasculogenic, odontoblastic, or neurogenic differentiation of human DPSCs for 7 d in vitro and performed single-cell RNA sequencing. Then, human DPSCs tagged with green fluorescent protein (DPSC-GFP) seeded in human tooth slice/scaffolds were transplanted into the subcutaneous space of immunodeficient mice. DPSC-GFP were sorted by flow cytometry 7 and 21 d after transplantation, and single-cell RNA sequencing was performed. In addition, a time course study was performed to investigate the sequence of differentiation events triggered upon transplantation of DPSC-GFP into mice. Here, we observed 8 distinct clusters of DPSCs at baseline, indicating a high level of cell heterogeneity. When DPSCs were induced to undergo vasculogenic, odontoblastic, or neurogenic differentiation in vitro , we observed distinct shifts in patterns of gene expression. Although some DPSCs retained mesenchymal stem cell markers likely due to asymmetric cell division and self-renewal, each differentiation protocol resulted in a unique gene expression signature. Stem cell markers that were highly expressed in DPSCs pretransplantation were progressively downregulated after 7 and 21 d in vivo. In contrast, endothelial cell markers presented high expression levels 7 d after transplantation, while neuronal markers showed upregulation 21 d after transplantation. Notably, while DPSC-derived functional blood vessels (i.e., blood-carrying vessels) can be clearly seen 2 wk after transplantation, well-defined DPSC-derived neural structures can be observed only after 5 wk. In conclusion, DPSCs are heterogeneous stem cells with distinct cell clusters, all of which contain progenitor cells with unique differentiation potential. Furthermore, this work demonstrated that microenvironment cues generated within human root canals are sufficient to induce vasculogenic differentiation, followed by neurogenic differentiation of DPSCs in vivo .
{"title":"Vasculogenic Precedes Neurogenic Differentiation in Dental Pulp Stem Cells","authors":"R. Tsuboi, Z. Zhang, K. Warner, S. The, E.T. Keller, J.E. Nör","doi":"10.1177/00220345251379776","DOIUrl":"https://doi.org/10.1177/00220345251379776","url":null,"abstract":"Dental pulp stem cells (DPSCs) are neural crest–derived stem cells endowed with multipotency and self-renewal. While processes orchestrating DPSC differentiation have been studied extensively, mechanisms underpinning the differentiation of human DPSCs <jats:italic toggle=\"yes\">in vivo</jats:italic> remain unclear. Here, we induced vasculogenic, odontoblastic, or neurogenic differentiation of human DPSCs for 7 d <jats:italic toggle=\"yes\">in vitro</jats:italic> and performed single-cell RNA sequencing. Then, human DPSCs tagged with green fluorescent protein (DPSC-GFP) seeded in human tooth slice/scaffolds were transplanted into the subcutaneous space of immunodeficient mice. DPSC-GFP were sorted by flow cytometry 7 and 21 d after transplantation, and single-cell RNA sequencing was performed. In addition, a time course study was performed to investigate the sequence of differentiation events triggered upon transplantation of DPSC-GFP into mice. Here, we observed 8 distinct clusters of DPSCs at baseline, indicating a high level of cell heterogeneity. When DPSCs were induced to undergo vasculogenic, odontoblastic, or neurogenic differentiation <jats:italic toggle=\"yes\">in vitro</jats:italic> , we observed distinct shifts in patterns of gene expression. Although some DPSCs retained mesenchymal stem cell markers likely due to asymmetric cell division and self-renewal, each differentiation protocol resulted in a unique gene expression signature. Stem cell markers that were highly expressed in DPSCs pretransplantation were progressively downregulated after 7 and 21 d in vivo. In contrast, endothelial cell markers presented high expression levels 7 d after transplantation, while neuronal markers showed upregulation 21 d after transplantation. Notably, while DPSC-derived functional blood vessels (i.e., blood-carrying vessels) can be clearly seen 2 wk after transplantation, well-defined DPSC-derived neural structures can be observed only after 5 wk. In conclusion, DPSCs are heterogeneous stem cells with distinct cell clusters, all of which contain progenitor cells with unique differentiation potential. Furthermore, this work demonstrated that microenvironment cues generated within human root canals are sufficient to induce vasculogenic differentiation, followed by neurogenic differentiation of DPSCs <jats:italic toggle=\"yes\">in vivo</jats:italic> .","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"64 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1177/00220345251384290
H Fang,P Li,Y Wei,H Li,P Wang,X Yang,H Yu,Y Fan,S Zhu,R Bi
Mandibular prognathism (MP) is the most common type of dentomaxillofacial deformity in East Asian populations. Genetic studies have revealed several MP-associated loci, suggesting that MP could be inherited as familial MP (fMP). However, functional verifications and in-depth mechanistic investigations of these loci are limited. For this study, we recruited 5 fMP families with 17 fMP members and 7 normal members. We first compared the clinical features of the 17 fMP members with 31 nonfamilial MP patients, finding a stronger mandibular overgrowth phenotype in the fMP subjects. Next, we performed whole-exome sequencing analysis with members of the 5 fMP families and singled out a potential fMP-associated pathogenic variant in the CASR gene (namely, rs117375173); the mutation introduces an amino acid substitution (A601G) in exon 7 and confers gain of function in Calcium-Sensing Receptor (CaSR). The rs11735173 variant changes the CaSR protein structure toward a semiactive state, similar to CaSR activated by L-tryptophan (L-Trp). To verify the regulating roles of CASR in mandibular bone growth, we further generated different mouse models with abnormal CaSR function. L-Trp administration effectively activated CaSR/GNAQ expression in vivo and in vitro. The MC3T3-E1 cell line transfected with CaSR with rs117375173 (CaSRA601G) showed increased osteogenic differentiation and collagen synthesis at the transcriptional level. Local injection of L-Trp in the mandible of growing mice significantly increased the mandibular length and BMD, due to activated osteogenic activity and suppressed bone resorption. At the same time, loss of function of CaSR in osteogenic progenitors caused mandibular growth retardation in Gli1-CreER; Casrfl/fl; tdTomatofl/+ mice. In conclusion, our study reveals that abnormal functioning of CaSR affects mandibular bone development and may contribute to the pathogenesis of fMP, providing a theoretical and experimental basis for the early diagnosis of and therapeutic strategies for fMP in clinical practice.
{"title":"CaSR Activation Triggers Mandibular Overgrowth in Familial Mandibular Prognathism Patients and Mice.","authors":"H Fang,P Li,Y Wei,H Li,P Wang,X Yang,H Yu,Y Fan,S Zhu,R Bi","doi":"10.1177/00220345251384290","DOIUrl":"https://doi.org/10.1177/00220345251384290","url":null,"abstract":"Mandibular prognathism (MP) is the most common type of dentomaxillofacial deformity in East Asian populations. Genetic studies have revealed several MP-associated loci, suggesting that MP could be inherited as familial MP (fMP). However, functional verifications and in-depth mechanistic investigations of these loci are limited. For this study, we recruited 5 fMP families with 17 fMP members and 7 normal members. We first compared the clinical features of the 17 fMP members with 31 nonfamilial MP patients, finding a stronger mandibular overgrowth phenotype in the fMP subjects. Next, we performed whole-exome sequencing analysis with members of the 5 fMP families and singled out a potential fMP-associated pathogenic variant in the CASR gene (namely, rs117375173); the mutation introduces an amino acid substitution (A601G) in exon 7 and confers gain of function in Calcium-Sensing Receptor (CaSR). The rs11735173 variant changes the CaSR protein structure toward a semiactive state, similar to CaSR activated by L-tryptophan (L-Trp). To verify the regulating roles of CASR in mandibular bone growth, we further generated different mouse models with abnormal CaSR function. L-Trp administration effectively activated CaSR/GNAQ expression in vivo and in vitro. The MC3T3-E1 cell line transfected with CaSR with rs117375173 (CaSRA601G) showed increased osteogenic differentiation and collagen synthesis at the transcriptional level. Local injection of L-Trp in the mandible of growing mice significantly increased the mandibular length and BMD, due to activated osteogenic activity and suppressed bone resorption. At the same time, loss of function of CaSR in osteogenic progenitors caused mandibular growth retardation in Gli1-CreER; Casrfl/fl; tdTomatofl/+ mice. In conclusion, our study reveals that abnormal functioning of CaSR affects mandibular bone development and may contribute to the pathogenesis of fMP, providing a theoretical and experimental basis for the early diagnosis of and therapeutic strategies for fMP in clinical practice.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"125 1","pages":"220345251384290"},"PeriodicalIF":7.6,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145434014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1177/00220345251382572
Z. Zou, J. Guo, J. Li, Y. Bao, W. Xie, Q. Hu, L. Wen, H. Lu, X. Liu, Q. Dong, J. Fang, Q. Hu, Y. Cao, Z. Wang, L. Yang, X. Wang
Immune alterations, such as neutrophil dysfunction, significantly affect the progression and outcome of periodontitis, a prevalent inflammatory disease. Despite this, the molecular mechanisms driving neutrophil dysregulation in periodontitis remain poorly understood. In this study, we demonstrate that CD300lf, a critical immune regulator, is markedly downregulated in neutrophils from a periodontitis mouse model and human patients. The loss of CD300lf accelerates neutrophil aging, as evidenced by increased reactive oxygen species production, the senescence-associated secretory phenotype with elevated IL-1β and S100A8/A9 levels, and heightened neutrophil extracellular trap formation. Mechanistically, CD300lf deficiency leads to MyD88 upregulation, indicating a shift toward a proinflammatory state. Inhibition of MyD88 effectively reduces periodontal inflammation in CD300lf-deficient mice. Furthermore, targeting CD300lf with its known ligand ceramide alleviates periodontitis and mitigates the aging phenotype of neutrophils. These findings underscore the critical role of the CD300lf/MyD88 axis in neutrophil homeostasis and suggest that modulation of CD300lf through ceramide presents a promising therapeutic strategy for periodontitis.
{"title":"CD300lf Regulates Neutrophil Aging and Periodontal Immune Homeostasis","authors":"Z. Zou, J. Guo, J. Li, Y. Bao, W. Xie, Q. Hu, L. Wen, H. Lu, X. Liu, Q. Dong, J. Fang, Q. Hu, Y. Cao, Z. Wang, L. Yang, X. Wang","doi":"10.1177/00220345251382572","DOIUrl":"https://doi.org/10.1177/00220345251382572","url":null,"abstract":"Immune alterations, such as neutrophil dysfunction, significantly affect the progression and outcome of periodontitis, a prevalent inflammatory disease. Despite this, the molecular mechanisms driving neutrophil dysregulation in periodontitis remain poorly understood. In this study, we demonstrate that CD300lf, a critical immune regulator, is markedly downregulated in neutrophils from a periodontitis mouse model and human patients. The loss of CD300lf accelerates neutrophil aging, as evidenced by increased reactive oxygen species production, the senescence-associated secretory phenotype with elevated IL-1β and S100A8/A9 levels, and heightened neutrophil extracellular trap formation. Mechanistically, CD300lf deficiency leads to MyD88 upregulation, indicating a shift toward a proinflammatory state. Inhibition of MyD88 effectively reduces periodontal inflammation in CD300lf-deficient mice. Furthermore, targeting CD300lf with its known ligand ceramide alleviates periodontitis and mitigates the aging phenotype of neutrophils. These findings underscore the critical role of the CD300lf/MyD88 axis in neutrophil homeostasis and suggest that modulation of CD300lf through ceramide presents a promising therapeutic strategy for periodontitis.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"1 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-24DOI: 10.1177/00220345251376974
Y-H Lee,J H Lee,Q-S Auh,S Lee,D Nixdorf,A Chaurasia
Temporomandibular disorders (TMDs) encompass a heterogeneous group of musculoskeletal conditions involving the temporomandibular joint (TMJ), masticatory muscles, and associated structures. Diagnosis remains challenging due to overlapping symptoms, multifactorial etiology, and variability across clinical settings. To address these limitations, we developed the Gated Attention Tabular Transformer (GATT), a novel deep-learning model that uses masked self-supervised learning and gated attention mechanisms, to classify TMD subgroups based on the diagnostic criteria for TMD (DC/TMD). A total of 4,644 structured clinical records from a university-based registry were analyzed, comprising 3,524 female and 1,120 male patients (mean age 36.9 ± 14.7 y), across 12 core TMD subgroups. GATT achieved robust diagnostic performance with area under the receiver-operating characteristic curve values ranging from 0.815 to 1.000, sensitivity from 0.652 to 1.000, and specificity from 0.773 to 1.000. The model significantly outperformed conventional machine-learning methods including logistic regression, random forest, support vector machine, and XGBoost as well as advanced tabular deep-learning models such as TabNet, TabTransformer, AutoGluon Tabular Predictor, and FT-Transformer. Shapley additive explanations (SHAP) analysis revealed "pain-free opening" (SHAP = 6.78, P < 0.001) and "current TMJ noise" (SHAP = 2.87, P = 0.003) as key features of mechanical TMJ disorders. Co-occurrence network analysis uncovered side-specific clustering and potential time-lagged progression between bilateral TMJs. These findings demonstrate the feasibility of using deep learning to classify heterogeneous TMD subgroups using only structured clinical data, without the need for imaging. The GATT model offers an accurate, explainable, and scalable tool to support clinician-assisted diagnosis and reduce variability in TMD management in real-world practice. These results support the integration of AI-driven tools such as GATT into clinical workflows for standardized, efficient, and patient-specific TMD diagnosis.
颞下颌关节疾病（TMDs）包括一组异质性的肌肉骨骼疾病，涉及颞下颌关节（TMJ）、咀嚼肌和相关结构。由于症状重叠、多因素病因和临床环境的可变性，诊断仍然具有挑战性。为了解决这些限制，我们开发了门控注意表转换器（GATT），这是一种新型的深度学习模型，使用屏蔽自监督学习和门控注意机制，根据TMD的诊断标准（DC/TMD）对TMD子组进行分类。研究人员分析了来自大学注册中心的4644份结构化临床记录，包括3524名女性和1120名男性患者（平均年龄36.9±14.7岁），涵盖12个核心TMD亚组。GATT具有较强的诊断能力，患者工作特征曲线下面积为0.815 ~ 1.000，灵敏度为0.652 ~ 1.000，特异度为0.773 ~ 1.000。该模型明显优于传统的机器学习方法，包括逻辑回归、随机森林、支持向量机和XGBoost，以及先进的表格深度学习模型，如TabNet、TabTransformer、AutoGluon tabular Predictor和FT-Transformer。Shapley加性解释（SHAP）分析显示，“无痛开口”（SHAP = 6.78, P < 0.001）和“当前TMJ噪声”（SHAP = 2.87, P = 0.003）是机械性TMJ障碍的主要特征。共发生网络分析揭示了两侧tmj之间的侧特异性聚类和潜在的时滞进展。这些发现表明，仅使用结构化临床数据，而无需成像，就可以使用深度学习对异质TMD亚组进行分类。GATT模型提供了一个准确的、可解释的、可扩展的工具，以支持临床辅助诊断，并减少现实世界实践中TMD管理的可变性。这些结果支持将GATT等人工智能驱动的工具整合到临床工作流程中，以实现标准化、高效和针对患者的TMD诊断。
{"title":"TMD Diagnosis Using a Masked Self-Supervised Tabular Transformer.","authors":"Y-H Lee,J H Lee,Q-S Auh,S Lee,D Nixdorf,A Chaurasia","doi":"10.1177/00220345251376974","DOIUrl":"https://doi.org/10.1177/00220345251376974","url":null,"abstract":"Temporomandibular disorders (TMDs) encompass a heterogeneous group of musculoskeletal conditions involving the temporomandibular joint (TMJ), masticatory muscles, and associated structures. Diagnosis remains challenging due to overlapping symptoms, multifactorial etiology, and variability across clinical settings. To address these limitations, we developed the Gated Attention Tabular Transformer (GATT), a novel deep-learning model that uses masked self-supervised learning and gated attention mechanisms, to classify TMD subgroups based on the diagnostic criteria for TMD (DC/TMD). A total of 4,644 structured clinical records from a university-based registry were analyzed, comprising 3,524 female and 1,120 male patients (mean age 36.9 ± 14.7 y), across 12 core TMD subgroups. GATT achieved robust diagnostic performance with area under the receiver-operating characteristic curve values ranging from 0.815 to 1.000, sensitivity from 0.652 to 1.000, and specificity from 0.773 to 1.000. The model significantly outperformed conventional machine-learning methods including logistic regression, random forest, support vector machine, and XGBoost as well as advanced tabular deep-learning models such as TabNet, TabTransformer, AutoGluon Tabular Predictor, and FT-Transformer. Shapley additive explanations (SHAP) analysis revealed \"pain-free opening\" (SHAP = 6.78, P < 0.001) and \"current TMJ noise\" (SHAP = 2.87, P = 0.003) as key features of mechanical TMJ disorders. Co-occurrence network analysis uncovered side-specific clustering and potential time-lagged progression between bilateral TMJs. These findings demonstrate the feasibility of using deep learning to classify heterogeneous TMD subgroups using only structured clinical data, without the need for imaging. The GATT model offers an accurate, explainable, and scalable tool to support clinician-assisted diagnosis and reduce variability in TMD management in real-world practice. These results support the integration of AI-driven tools such as GATT into clinical workflows for standardized, efficient, and patient-specific TMD diagnosis.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"3 1","pages":"220345251376974"},"PeriodicalIF":7.6,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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