Pub Date : 2026-01-05DOI: 10.1001/jamapediatrics.2025.5450
Avihu Z. Gazit, Steven M. Schwartz, Joshua W. Salvin, Peter C. Laussen
This Viewpoint examines the gap between the development of artificial intelligence (AI) models in pediatric intensive care and their use in clinical practice and proposes deployment of AI models during specific phases of care.
{"title":"AI in Critical Care—Use for De-Escalation Rather Than Escalation of Care","authors":"Avihu Z. Gazit, Steven M. Schwartz, Joshua W. Salvin, Peter C. Laussen","doi":"10.1001/jamapediatrics.2025.5450","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2025.5450","url":null,"abstract":"This Viewpoint examines the gap between the development of artificial intelligence (AI) models in pediatric intensive care and their use in clinical practice and proposes deployment of AI models during specific phases of care.","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"79 1","pages":""},"PeriodicalIF":26.1,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897753","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 : 2026-01-05DOI: 10.1001/jamapediatrics.2025.5485
Kyung E Rhee,Tanayott Thaweethai,Deepti B Pant,Cheryl R Stein,Amy L Salisbury,Patricia A Kinser,Lawrence C Kleinman,Richard Gallagher,David Warburton,Sindhu Mohandas,Jessica N Snowden,Melissa S Stockwell,Kelan G Tantisira,Valerie J Flaherman,Ronald J Teufel,Leah Castro,Alicia Chung,Jocelyn Espinoza Esparza,Christine W Hockett,Maria Isidoro-Chino,Anita Krishnan,Lacey A McCormack,Aleisha M Nabower,Erica R Nahin,Johana M Rosas,Sarwat Siddiqui,Jacqueline R Szmuszkovicz,Nita Vangeepuram,Emily Zimmerman,Heather-Elizabeth Brown,Megan Carmilani,K Coombs,Liza Fisher,Margot Gage Witvliet,John C Wood,Joshua D Milner,Erika B Rosenzweig,Katherine Irby,Elizabeth W Karlson,Zihan Qian,Michelle F Lamendola-Essel,Denise C Hasson,Stuart D Katz,H Shonna Yin,Andrea S Foulkes,Rachel S Gross, ,Judy L Aschner,Andrew M Atz,Dithi Banerjee,Amanda Bogie,Hulya Bukulmez,Katharine Clouser,Lesley A Cottrell,Kelly Cowan,Viren A D'Sa,Allen J Dozor,Amy J Elliott,E Vince S Faustino,Alexander G Fiks,Sunanda Gaur,Maria L Gennaro,Stewart T Gordon,Uzma N Hasan,Christina M Hester,Alexander H Hogan,Daniel S Hsia,David C Kaelber,Jessica S Kosut,Sankaran Krishnan,Russell J McCulloh,Ian C Michelow,Sheila M Nolan,Carlos R Oliveira,Wilson D Pace,Paul Palumbo,Hengameh Raissy,Andy Reyes,Judith L Ross,Juan C Salazar,Rangaraj Selvarangan,Michelle D Stevenson,Alan Werzberger,John M Westfall,Kathleen Zani,William T Zempsky,James Chan,Torri D Metz,Jane W Newburger,Dongngan T Truong,Candace H Feldman,Robin Aupperle,Fiona C Baker,Marie T Banich,Deanna M Barch,Arielle Baskin-Sommers,James M Bjork,Mirella Dapretto,Sandra A Brown,B J Casey,Linda Chang,Duncan B Clark,Anders M Dale,Thomas M Ernst,Damien A Fair,Sarah W Feldstein Ewing,John J Foxe,Edward G Freedman,Naomi P Friedman,Hugh Garavan,Dylan G Gee,Raul Gonzalez,Kevin M Gray,Mary M Heitzeg,Megan M Herting,Joanna Jacobus,Angela R Laird,Christine L Larson,Krista M Lisdahl,Monica Luciana,Beatriz Luna,Pamela A F Madden,Erin C McGlade,Eva M Müller-Oehring,Bonnie J Nagel,Michael C Neale,Martin P Paulus,Alexandra S Potter,Perry F Renshaw,Elizabeth R Sowell,Lindsay M Squeglia,Lucina Q Uddin,Sylia Wilson,Deborah A Yurgelun-Todd
ImportanceMillions of children worldwide are experiencing prolonged symptoms after SARS-CoV-2 infection, yet social risk factors for developing long COVID are largely unknown. As child health is influenced by the environment in which they live and interact, adverse social determinants of health (SDOH) may contribute to the development of pediatric long COVID.ObjectiveTo identify whether adverse SDOH are associated with increased odds of long COVID in school-aged children and adolescents in the US.Design, Setting, and ParticipantsThis cross-sectional analysis of a multicenter, longitudinal, meta-cohort study encompassed 52 sites (health care and community settings) across the US. School-aged children (6-11 years; n = 903) and adolescents (12-17 years; n = 3681) with SARS-CoV-2 infection history were included. Those with an unknown date of first infection, history of multisystem inflammatory syndrome in children, or symptom surveys with less than 50% of questions completed were excluded. Participants were recruited via health care systems, long COVID clinics, fliers, websites, social media campaigns, radio, health fairs, community-based organizations, community health workers, and existing research cohorts from March 2022 to August 2024, and surveys were completed by caregivers between March 2022 and August 2024.ExposureTwenty-four individual social determinant of health factors were grouped into 5 Healthy People 2030 domains: economic stability, social and community context, caregiver education access and quality, neighborhood and built environment, and health care access and quality. Latent classes were created within each domain and used in regression models.Main Outcomes and MeasuresPresence of long COVID using caregiver-reported, symptom-based, age-specific research indices.ResultsThe mean (SD) age among 4584 individuals included in this study was 14 (3) years, and 2330 (51%) of participants were male. The number of latent classes varied by domain; the reference group was the class with the least adversity. In unadjusted analyses, most classes in each domain were associated with higher odds of long COVID. After adjusting for many factors, including age group, sex, timing of infection, referral source, and other social determinant of health domains, economic instability characterized by difficulty covering expenses, poverty, receipt of government assistance, and food insecurity were associated with an increased risk of having long COVID (class 2 adjusted odds ratio [aOR], 1.57; 95% CI, 1.18-2.09; class 4 aOR, 2.39; 95% CI, 1.73-3.30); economic instability without food insecurity (class 3) was not (aOR, 0.93; 95% CI, 0.70-1.23). Poorer social and community context (eg, high levels of discrimination and low social support) was also associated with long COVID (aOR, 2.17; 95% CI, 1.77-2.66). Sensitivity analyses stratified by age group and adjusted for race and ethnicity did not alter or attenuate these results.Conclusions and RelevanceIn this stud
{"title":"Social Determinants of Health and Pediatric Long COVID in the US.","authors":"Kyung E Rhee,Tanayott Thaweethai,Deepti B Pant,Cheryl R Stein,Amy L Salisbury,Patricia A Kinser,Lawrence C Kleinman,Richard Gallagher,David Warburton,Sindhu Mohandas,Jessica N Snowden,Melissa S Stockwell,Kelan G Tantisira,Valerie J Flaherman,Ronald J Teufel,Leah Castro,Alicia Chung,Jocelyn Espinoza Esparza,Christine W Hockett,Maria Isidoro-Chino,Anita Krishnan,Lacey A McCormack,Aleisha M Nabower,Erica R Nahin,Johana M Rosas,Sarwat Siddiqui,Jacqueline R Szmuszkovicz,Nita Vangeepuram,Emily Zimmerman,Heather-Elizabeth Brown,Megan Carmilani,K Coombs,Liza Fisher,Margot Gage Witvliet,John C Wood,Joshua D Milner,Erika B Rosenzweig,Katherine Irby,Elizabeth W Karlson,Zihan Qian,Michelle F Lamendola-Essel,Denise C Hasson,Stuart D Katz,H Shonna Yin,Andrea S Foulkes,Rachel S Gross, ,Judy L Aschner,Andrew M Atz,Dithi Banerjee,Amanda Bogie,Hulya Bukulmez,Katharine Clouser,Lesley A Cottrell,Kelly Cowan,Viren A D'Sa,Allen J Dozor,Amy J Elliott,E Vince S Faustino,Alexander G Fiks,Sunanda Gaur,Maria L Gennaro,Stewart T Gordon,Uzma N Hasan,Christina M Hester,Alexander H Hogan,Daniel S Hsia,David C Kaelber,Jessica S Kosut,Sankaran Krishnan,Russell J McCulloh,Ian C Michelow,Sheila M Nolan,Carlos R Oliveira,Wilson D Pace,Paul Palumbo,Hengameh Raissy,Andy Reyes,Judith L Ross,Juan C Salazar,Rangaraj Selvarangan,Michelle D Stevenson,Alan Werzberger,John M Westfall,Kathleen Zani,William T Zempsky,James Chan,Torri D Metz,Jane W Newburger,Dongngan T Truong,Candace H Feldman,Robin Aupperle,Fiona C Baker,Marie T Banich,Deanna M Barch,Arielle Baskin-Sommers,James M Bjork,Mirella Dapretto,Sandra A Brown,B J Casey,Linda Chang,Duncan B Clark,Anders M Dale,Thomas M Ernst,Damien A Fair,Sarah W Feldstein Ewing,John J Foxe,Edward G Freedman,Naomi P Friedman,Hugh Garavan,Dylan G Gee,Raul Gonzalez,Kevin M Gray,Mary M Heitzeg,Megan M Herting,Joanna Jacobus,Angela R Laird,Christine L Larson,Krista M Lisdahl,Monica Luciana,Beatriz Luna,Pamela A F Madden,Erin C McGlade,Eva M Müller-Oehring,Bonnie J Nagel,Michael C Neale,Martin P Paulus,Alexandra S Potter,Perry F Renshaw,Elizabeth R Sowell,Lindsay M Squeglia,Lucina Q Uddin,Sylia Wilson,Deborah A Yurgelun-Todd","doi":"10.1001/jamapediatrics.2025.5485","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2025.5485","url":null,"abstract":"ImportanceMillions of children worldwide are experiencing prolonged symptoms after SARS-CoV-2 infection, yet social risk factors for developing long COVID are largely unknown. As child health is influenced by the environment in which they live and interact, adverse social determinants of health (SDOH) may contribute to the development of pediatric long COVID.ObjectiveTo identify whether adverse SDOH are associated with increased odds of long COVID in school-aged children and adolescents in the US.Design, Setting, and ParticipantsThis cross-sectional analysis of a multicenter, longitudinal, meta-cohort study encompassed 52 sites (health care and community settings) across the US. School-aged children (6-11 years; n = 903) and adolescents (12-17 years; n = 3681) with SARS-CoV-2 infection history were included. Those with an unknown date of first infection, history of multisystem inflammatory syndrome in children, or symptom surveys with less than 50% of questions completed were excluded. Participants were recruited via health care systems, long COVID clinics, fliers, websites, social media campaigns, radio, health fairs, community-based organizations, community health workers, and existing research cohorts from March 2022 to August 2024, and surveys were completed by caregivers between March 2022 and August 2024.ExposureTwenty-four individual social determinant of health factors were grouped into 5 Healthy People 2030 domains: economic stability, social and community context, caregiver education access and quality, neighborhood and built environment, and health care access and quality. Latent classes were created within each domain and used in regression models.Main Outcomes and MeasuresPresence of long COVID using caregiver-reported, symptom-based, age-specific research indices.ResultsThe mean (SD) age among 4584 individuals included in this study was 14 (3) years, and 2330 (51%) of participants were male. The number of latent classes varied by domain; the reference group was the class with the least adversity. In unadjusted analyses, most classes in each domain were associated with higher odds of long COVID. After adjusting for many factors, including age group, sex, timing of infection, referral source, and other social determinant of health domains, economic instability characterized by difficulty covering expenses, poverty, receipt of government assistance, and food insecurity were associated with an increased risk of having long COVID (class 2 adjusted odds ratio [aOR], 1.57; 95% CI, 1.18-2.09; class 4 aOR, 2.39; 95% CI, 1.73-3.30); economic instability without food insecurity (class 3) was not (aOR, 0.93; 95% CI, 0.70-1.23). Poorer social and community context (eg, high levels of discrimination and low social support) was also associated with long COVID (aOR, 2.17; 95% CI, 1.77-2.66). Sensitivity analyses stratified by age group and adjusted for race and ethnicity did not alter or attenuate these results.Conclusions and RelevanceIn this stud","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"84 1","pages":""},"PeriodicalIF":26.1,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903552","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 : 2026-01-05DOI: 10.1001/jamapediatrics.2025.5443
Daniel R. Arnold, Megha Reddy, Jonathan Cantor, Ryan K. McBain, Hao Yu, Christopher M. Whaley, Yashaswini Singh
This economic evaluation reports the number of therapeutic services for autism spectrum disorder that were acquired by private equity companies between 2015 and 2024.
这份经济评估报告了2015年至2024年间私募股权公司收购的自闭症谱系障碍治疗服务的数量。
{"title":"Private Equity in Autism Services","authors":"Daniel R. Arnold, Megha Reddy, Jonathan Cantor, Ryan K. McBain, Hao Yu, Christopher M. Whaley, Yashaswini Singh","doi":"10.1001/jamapediatrics.2025.5443","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2025.5443","url":null,"abstract":"This economic evaluation reports the number of therapeutic services for autism spectrum disorder that were acquired by private equity companies between 2015 and 2024.","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"94 1","pages":""},"PeriodicalIF":26.1,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897819","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 : 2026-01-05DOI: 10.1001/jamapediatrics.2025.5385
Jay G. Berry, Derek Mathieu, Steven J. Staffa, Ben Y. Reis, Peter Hong, Gabor Asztalos, Lynne Ferrari
Importance Hospitals are increasingly experiencing challenges with variable and unpredictable inpatient loads, including days with excessively high and excessively low capacity for surgical patients. Artificial intelligence has the potential to facilitate postoperative hospital bed management and stabilize capacity. Objectives To predict hospital length of stay (LOS) following elective surgical procedures using machine learning methods, and to implement the LOS prediction model in a perioperative clinical setting and evaluate its ability to optimize elective surgical scheduling and hospital bed capacity. Design, Setting, and Participants This preimplementation and postimplementation cohort study was conducted at a tertiary, freestanding, US children’s hospital among patients of any age undergoing an elective surgical procedure requiring inpatient recovery. For LOS prediction, a retrospective analysis was performed on elective surgical cases from January 1, 2018, to March 31, 2022, using Extreme Gradient Boosting (XGBoost) to predict postoperative LOS based on in-training and holdout datasets, with hyperparameter tuning using 5-fold cross-validation. For implementation and evaluation of the LOS prediction model, a preimplementation and postimplementation analysis was performed from July 1, 2022, to April 30, 2024. Data analysis was conducted from June 1 to October 31, 2024. Exposures Patients’ type of surgery, chronic conditions, and demographic characteristics. Main Outcomes and Measures Postoperative LOS, day-to-day variance in bedded days for elective surgical procedures, and days with excessively high capacity (&gt;75th percentile of historical elective surgical census) or excessively low capacity (&lt;25th percentile of historical elective surgical census). Results There were 21 352 elective surgical cases (mean [SD] age, 10.2 [7.4] years; 10 804 [50.6%] female) for patients included in the retrospective analysis of postoperative LOS prediction and 12 522 elective surgical cases in the pretest and posttest analysis of the prediction model (premodel implementation, n = 5867; postmodel implementation, n = 6655). The postoperative LOS model had 85.6% accuracy with a 1-night leniency. The model’s mean absolute error was 0.6 days. After implementation of the LOS model in elective surgery scheduling and hospital bed capacity management, the median number of elective surgical procedures increased by 5 (IQR, 4.5-5) for each weekday. Variation in postoperative bedded days across days of the week decreased significantly. The magnitude of the IQR of bedded days decreased the most during midweek: 43% and 44% reductions in the IQR occurred on Wednesdays and Thursdays, respectively. The percentage of weekdays with underused capacity (&lt;84 patients) decreased from 33% to 10% ( <jats:italic toggle="yes">P</jats:italic> &lt; .001), without a significant increase in days with excessive capacity. Conclusions and Relevance In th
{"title":"Artificial Intelligence Length-of-Stay Forecasting and Pediatric Surgical Capacity","authors":"Jay G. Berry, Derek Mathieu, Steven J. Staffa, Ben Y. Reis, Peter Hong, Gabor Asztalos, Lynne Ferrari","doi":"10.1001/jamapediatrics.2025.5385","DOIUrl":"https://doi.org/10.1001/jamapediatrics.2025.5385","url":null,"abstract":"Importance Hospitals are increasingly experiencing challenges with variable and unpredictable inpatient loads, including days with excessively high and excessively low capacity for surgical patients. Artificial intelligence has the potential to facilitate postoperative hospital bed management and stabilize capacity. Objectives To predict hospital length of stay (LOS) following elective surgical procedures using machine learning methods, and to implement the LOS prediction model in a perioperative clinical setting and evaluate its ability to optimize elective surgical scheduling and hospital bed capacity. Design, Setting, and Participants This preimplementation and postimplementation cohort study was conducted at a tertiary, freestanding, US children’s hospital among patients of any age undergoing an elective surgical procedure requiring inpatient recovery. For LOS prediction, a retrospective analysis was performed on elective surgical cases from January 1, 2018, to March 31, 2022, using Extreme Gradient Boosting (XGBoost) to predict postoperative LOS based on in-training and holdout datasets, with hyperparameter tuning using 5-fold cross-validation. For implementation and evaluation of the LOS prediction model, a preimplementation and postimplementation analysis was performed from July 1, 2022, to April 30, 2024. Data analysis was conducted from June 1 to October 31, 2024. Exposures Patients’ type of surgery, chronic conditions, and demographic characteristics. Main Outcomes and Measures Postoperative LOS, day-to-day variance in bedded days for elective surgical procedures, and days with excessively high capacity (&amp;gt;75th percentile of historical elective surgical census) or excessively low capacity (&amp;lt;25th percentile of historical elective surgical census). Results There were 21 352 elective surgical cases (mean [SD] age, 10.2 [7.4] years; 10 804 [50.6%] female) for patients included in the retrospective analysis of postoperative LOS prediction and 12 522 elective surgical cases in the pretest and posttest analysis of the prediction model (premodel implementation, n = 5867; postmodel implementation, n = 6655). The postoperative LOS model had 85.6% accuracy with a 1-night leniency. The model’s mean absolute error was 0.6 days. After implementation of the LOS model in elective surgery scheduling and hospital bed capacity management, the median number of elective surgical procedures increased by 5 (IQR, 4.5-5) for each weekday. Variation in postoperative bedded days across days of the week decreased significantly. The magnitude of the IQR of bedded days decreased the most during midweek: 43% and 44% reductions in the IQR occurred on Wednesdays and Thursdays, respectively. The percentage of weekdays with underused capacity (&amp;lt;84 patients) decreased from 33% to 10% ( <jats:italic toggle=\"yes\">P</jats:italic> &amp;lt; .001), without a significant increase in days with excessive capacity. Conclusions and Relevance In th","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"19 1","pages":""},"PeriodicalIF":26.1,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897754","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 : 2026-01-01DOI: 10.1001/jamapediatrics.2025.5835
{"title":"Change to Open Access.","authors":"","doi":"10.1001/jamapediatrics.2025.5835","DOIUrl":"10.1001/jamapediatrics.2025.5835","url":null,"abstract":"","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":"180 1","pages":"118"},"PeriodicalIF":18.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12771226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1001/jamapediatrics.2025.4534
Aditi Vasan, Jeffrey P Brosco
{"title":"Medicaid and Child Health-Threats and Opportunities.","authors":"Aditi Vasan, Jeffrey P Brosco","doi":"10.1001/jamapediatrics.2025.4534","DOIUrl":"10.1001/jamapediatrics.2025.4534","url":null,"abstract":"","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":" ","pages":"15-17"},"PeriodicalIF":18.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145540807","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 : 2026-01-01DOI: 10.1001/jamapediatrics.2025.5223
{"title":"Errors in Letter and Supplement.","authors":"","doi":"10.1001/jamapediatrics.2025.5223","DOIUrl":"10.1001/jamapediatrics.2025.5223","url":null,"abstract":"","PeriodicalId":14683,"journal":{"name":"JAMA Pediatrics","volume":" ","pages":"118"},"PeriodicalIF":18.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12645393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145587452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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