Pub Date : 2026-01-21DOI: 10.1097/CORR.0000000000003798
Charles A C Villamin
{"title":"CORR Insights®: Periacetabular Resection for Bone Tumors: Is There Still a Role for Massive Allograft-prosthesis Composite Reconstructions?","authors":"Charles A C Villamin","doi":"10.1097/CORR.0000000000003798","DOIUrl":"10.1097/CORR.0000000000003798","url":null,"abstract":"","PeriodicalId":10404,"journal":{"name":"Clinical Orthopaedics and Related Research®","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146050479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1097/corr.0000000000003829
Christoph A Schroen
{"title":"Letter to the Editor: What Is the Probability of Radial Nerve Recovery After Surgical Repair of Humerus Fractures Accounting for Time Since Injury?","authors":"Christoph A Schroen","doi":"10.1097/corr.0000000000003829","DOIUrl":"https://doi.org/10.1097/corr.0000000000003829","url":null,"abstract":"","PeriodicalId":10404,"journal":{"name":"Clinical Orthopaedics and Related Research®","volume":"6 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1097/corr.0000000000003826
Robin T Higashi,Jessica Lee,Yadira Hernandez,Marisel Pontón,Joshua M Liao,Kyle C Cross,Jessica I Billig
BACKGROUNDTraumatic hand injuries can result in substantial harms to patients' functional abilities and financial health, disproportionately affecting individuals who are of working age, low income, and uninsured. However, little is known about how patients and their families cope with the functional limitations and economic consequences that follow, and how these injuries affect patients' mental and emotional health. Exploring clinician and staff perceptions about the impact of traumatic hand injuries on patients may provide insight into how to help address the various burdens patients face.QUESTIONS/PURPOSESIn this paper, we aimed to explore the following questions: (1) What is the impact of a traumatic hand injury on patients' financial health? (2) How does a traumatic hand injury affect patients' physical health and disability? (3) What kind of toll does a traumatic hand injury take on patients' emotional and mental health? (4) What health system challenges function as additional stressors after a traumatic hand injury?METHODSThis descriptive study consisted of surveys and semistructured interviews. We recruited patients from an outpatient hand surgery clinic at a safety-net hospital in a southern US city. Eligible patients were English- or Spanish-speaking, age 18 years or older, who presented to the clinic after a traumatic hand injury. Between January and April 2025, we invited patients to participate in three surveys that assessed financial burden and upper extremity disability: the Comprehensive Score for Financial Toxicity-Functional Assessment of Chronic Illness Therapy (COST-FACIT), the DASH questionnaire, and the InCharge Financial Distress/Financial Well-Being Scale. Of 94 surveys offered, 88% (83) of surveys were completed. The mean ± SD patient age was 38 ± 13 years. Forty-five percent (37 of 83) of participants spoke English, 55% (46 of 83) spoke Spanish, 82% (68 of 83) identified as Hispanic or Latino (any race), 70% (58 of 83) were male, and 58% (48 of 83) relied on the safety-net institution's county charity program for coverage of medical expenses. We completed 26 interviews with patients and 10 interviews with clinicians and staff (three physicians, three clinical staff, one nonclinical staff, and three finance staff), each lasting a median (range) of 24 minutes (13 to 54), while 11 patients and four clinicians and staff declined to participate because of lack of time or interest. Among interview participants, the mean ± SD participant age was 39 ± 15 years. Fifty-four percent (14 of 26) spoke English, 58% (15 of 26) identified as Hispanic or Latino (any race), 65% (17 of 26) were male, and 42% (11 of 26) relied on the county charity program. All statistical analyses were performed using R, version 4.5.0, with p < 0.05. A convenience sample of clinic patients was invited to participate in semistructured interviews, as was a purposive sample of clinicians and staff. Eligible clinicians and staff were those who had been employ
{"title":"How Does Traumatic Hand Injury Impact Patients in a Safety-net Healthcare System? A Mixed Methods Study.","authors":"Robin T Higashi,Jessica Lee,Yadira Hernandez,Marisel Pontón,Joshua M Liao,Kyle C Cross,Jessica I Billig","doi":"10.1097/corr.0000000000003826","DOIUrl":"https://doi.org/10.1097/corr.0000000000003826","url":null,"abstract":"BACKGROUNDTraumatic hand injuries can result in substantial harms to patients' functional abilities and financial health, disproportionately affecting individuals who are of working age, low income, and uninsured. However, little is known about how patients and their families cope with the functional limitations and economic consequences that follow, and how these injuries affect patients' mental and emotional health. Exploring clinician and staff perceptions about the impact of traumatic hand injuries on patients may provide insight into how to help address the various burdens patients face.QUESTIONS/PURPOSESIn this paper, we aimed to explore the following questions: (1) What is the impact of a traumatic hand injury on patients' financial health? (2) How does a traumatic hand injury affect patients' physical health and disability? (3) What kind of toll does a traumatic hand injury take on patients' emotional and mental health? (4) What health system challenges function as additional stressors after a traumatic hand injury?METHODSThis descriptive study consisted of surveys and semistructured interviews. We recruited patients from an outpatient hand surgery clinic at a safety-net hospital in a southern US city. Eligible patients were English- or Spanish-speaking, age 18 years or older, who presented to the clinic after a traumatic hand injury. Between January and April 2025, we invited patients to participate in three surveys that assessed financial burden and upper extremity disability: the Comprehensive Score for Financial Toxicity-Functional Assessment of Chronic Illness Therapy (COST-FACIT), the DASH questionnaire, and the InCharge Financial Distress/Financial Well-Being Scale. Of 94 surveys offered, 88% (83) of surveys were completed. The mean ± SD patient age was 38 ± 13 years. Forty-five percent (37 of 83) of participants spoke English, 55% (46 of 83) spoke Spanish, 82% (68 of 83) identified as Hispanic or Latino (any race), 70% (58 of 83) were male, and 58% (48 of 83) relied on the safety-net institution's county charity program for coverage of medical expenses. We completed 26 interviews with patients and 10 interviews with clinicians and staff (three physicians, three clinical staff, one nonclinical staff, and three finance staff), each lasting a median (range) of 24 minutes (13 to 54), while 11 patients and four clinicians and staff declined to participate because of lack of time or interest. Among interview participants, the mean ± SD participant age was 39 ± 15 years. Fifty-four percent (14 of 26) spoke English, 58% (15 of 26) identified as Hispanic or Latino (any race), 65% (17 of 26) were male, and 42% (11 of 26) relied on the county charity program. All statistical analyses were performed using R, version 4.5.0, with p < 0.05. A convenience sample of clinic patients was invited to participate in semistructured interviews, as was a purposive sample of clinicians and staff. Eligible clinicians and staff were those who had been employ","PeriodicalId":10404,"journal":{"name":"Clinical Orthopaedics and Related Research®","volume":"52 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1097/corr.0000000000003824
Debra Zillmer
{"title":"The Intersection of Orthopaedic Culture and Gender: Too Confident.","authors":"Debra Zillmer","doi":"10.1097/corr.0000000000003824","DOIUrl":"https://doi.org/10.1097/corr.0000000000003824","url":null,"abstract":"","PeriodicalId":10404,"journal":{"name":"Clinical Orthopaedics and Related Research®","volume":"95 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1097/corr.0000000000003828
Stijn R J Mennes,Sebastian Engbers,Bjarty L Garcia,Reinier W A Spek,Roelina Munnik-Hagewoud,Rutger G Zuurmond,Ruurd L Jaarsma,Job N Doornberg,Michel P J van den Bekerom,
BACKGROUNDProximal humerus fractures (PHFs) in patients ≥ 65 years of age are associated with increased risk of death in the months after injury. Controversy exists regarding the preferred treatment strategy in these patients, and operative treatment is associated with high complication and reoperation rates. Machine learning (ML)-driven probability calculators for mortality prediction therefore may be valuable during shared decision-making for surgeons and patients.QUESTIONS/PURPOSES(1) To develop ML algorithms to predict 1-year mortality in patients ≥ 65 years of age. (2) To externally validate all algorithms on a geographically distinct patient population. (3) To create an easy-to-use, online calculator that can be used by surgeons at the point of care to enable more informed decision-making.METHODSThis study identified 5114 potentially eligible patients age ≥ 65 years who presented to our two hospitals in Holland (one is a Level 1 trauma center and one is a Level 2 trauma center) between January 2016 and December 2023. Of those, we considered 3488 patients eligible because they were ≥ 65 years of age and had a first-time PHF. Based on that, 86% (2999) were included for the analysis. A further 10% (334) were excluded because of misdiagnosis, bilateral PHFs, or a history of previous PHFs. Finally, 4% (155) had an irretrievable mortality status or had incomplete data sets. Data on 24 potential factors associated with increased mortality after PHFs were collected. Surgical or nonoperative treatment were not included as the aim was to predict 1-year mortality at the moment a PHF was sustained, before a treatment choice had been made. Therefore, excluding treatment modalities does not limit the intended use as a pretreatment risk estimation model. Four ML algorithms were developed: logistic regression, extreme gradient boosting machine (XGBoost), random forest, and LightGBM. The ML algorithms were trained and internally validated on patients from the first hospital (59% [1768 of 2999]) and externally validated on a geographically distinct group of patients from the second hospital (41% [1231 of 2999]). The mean ± SD age in the training cohort was 77 ± 8 years, and it was 76 ± 8 years in the external validation set; 79% (2383 of 2999) of patients were female. The overall 1-year mortality rate was 11% (325 of 2999). Performance was assessed with discrimination and calibration curves, and overall performance was assessed using the Brier score. Discrimination was assessed with the c-statistic: the area under the receiver operating characteristic curve. The c-statistic ranges from 0.50 to 1.0, with 1.0 indicating perfect discriminating ability. Calibration was assessed by plotting the agreement between the observed outcome and predicted probability, and the intercept and slope were determined. The plot's intercept indicates whether predictions were too high (intercept < 0) or too low (intercept > 0). The slope reflects either overfitting (predictions to
背景：≥65岁的患者肱骨近端骨折（phf）与损伤后几个月内死亡风险增加相关。对于这些患者的首选治疗策略存在争议，手术治疗与高并发症和再手术率相关。因此，机器学习（ML）驱动的死亡率预测概率计算器在外科医生和患者共同决策时可能很有价值。(1)开发ML算法来预测≥65岁患者的1年死亡率。(2)在地理上不同的患者群体上对所有算法进行外部验证。(3)创建一个易于使用的在线计算器，供外科医生在护理点使用，以实现更明智的决策。方法：在2016年1月至2023年12月期间，在荷兰的两家医院（一家是一级创伤中心，一家是二级创伤中心）就诊的5114名年龄≥65岁的潜在符合条件的患者。其中，我们认为3488例患者符合条件，因为他们年龄≥65岁且首次发生PHF。在此基础上，86%（2999）被纳入分析。另有10%（334例）因误诊、双侧PHFs或既往PHFs病史而被排除。最后，4%（155例）的死亡率不可挽回或数据集不完整。收集了与phf后死亡率增加相关的24个潜在因素的数据。手术或非手术治疗不包括在内，因为其目的是在治疗选择之前预测PHF持续的1年死亡率。因此，排除治疗方式并不限制其作为预处理风险评估模型的预期用途。开发了四种机器学习算法：逻辑回归、极端梯度增强机（XGBoost）、随机森林和LightGBM。机器学习算法在第一家医院的患者身上进行了训练和内部验证（59%[2999的1768人]），在第二家医院的地理位置不同的患者组上进行了外部验证（41%[2999的1231人]）。训练组的平均±SD年龄为77±8岁，外部验证组的平均±SD年龄为76±8岁；2999例患者中女性占79%（2383例）。总的1年死亡率为11%（2999例中有325例）。用判别曲线和校准曲线评估其表现，用Brier评分评估其总体表现。用c统计量（即受者工作特征曲线下的面积）评价鉴别性。c统计量在0.50 ~ 1.0之间，1.0表示判别能力较好。通过绘制观测结果与预测概率之间的一致性来评估校准，并确定截距和斜率。图的截距表明预测是过高（截距< 0）还是过低（截距> 0）。斜率反映了过拟合（预测过于极端，斜率> 1）或欠拟合（预测不够极端，斜率< 1）。一个理想的预测模型具有截距为0，斜率为1的校准曲线。Brier分数反映了整体表现，是判别和校准的综合表现。0分代表完美预测，1分代表最差预测。阴性和阳性预测值也进行了评估。对于内部验证，执行五次交叉验证以防止数据泄漏，并使用1000次引导来确保稳健的结果并解释乐观主义。交叉验证需要将训练集划分为子集（五个），然后在四个集合上训练模型。第五，看不见的集合用于内部验证，防止高估模型性能。对于外部验证，仅使用1000倍的引导来评估性能，以确保稳健的结果和正确的乐观主义。结果算法与c-statistics（判别能力）相似，内部验证的范围为0.80 ~ 0.81(95%置信区间[CI] 0.72 ~ 0.86)，外部验证的范围为0.83 ~ 0.85 （95% CI 0.81 ~ 0.86）。c统计量超过0.80被认为是老年创伤人群死亡率预测模型的强大性能。在评估的模型中选择逻辑回归作为最佳模型，因为它具有足够的校准和可解释性。强校正确保模型不受过拟合或欠拟合的影响，也不会预测过高或过低。逻辑回归是可解释的，因为它需要较少的预测因子并提供可理解的系数。阴性预测值为0.91 (95% CI 0.90至0.92)，阳性预测值为0.66 (95% CI 0.54至0.81)，与死亡率相关性最强的因素是偏瘫、骨折前在医疗机构的居住和心力衰竭。 本研究开发并外部验证了一种机器学习驱动的预测模型，该模型可以准确地提供单个患者的1年死亡风险。医生可以在共同决策和患者咨询时使用这种预测预后的工具，因为它在考虑phf治疗方案时为患者和家属提供了现实的期望，从而增强了知情同意过程。预测工具被整合到一个免费的web应用程序中，可以通过https://bjarty.shinyapps.io/mortality_app/.LEVEL OF EVIDENCELevel III，治疗性研究访问。
{"title":"Machine Learning-driven Probability Calculators Can Accurately Predict 1-year Mortality After Proximal Humerus Fractures in Patients Over the Age of 65 Years.","authors":"Stijn R J Mennes,Sebastian Engbers,Bjarty L Garcia,Reinier W A Spek,Roelina Munnik-Hagewoud,Rutger G Zuurmond,Ruurd L Jaarsma,Job N Doornberg,Michel P J van den Bekerom, ","doi":"10.1097/corr.0000000000003828","DOIUrl":"https://doi.org/10.1097/corr.0000000000003828","url":null,"abstract":"BACKGROUNDProximal humerus fractures (PHFs) in patients ≥ 65 years of age are associated with increased risk of death in the months after injury. Controversy exists regarding the preferred treatment strategy in these patients, and operative treatment is associated with high complication and reoperation rates. Machine learning (ML)-driven probability calculators for mortality prediction therefore may be valuable during shared decision-making for surgeons and patients.QUESTIONS/PURPOSES(1) To develop ML algorithms to predict 1-year mortality in patients ≥ 65 years of age. (2) To externally validate all algorithms on a geographically distinct patient population. (3) To create an easy-to-use, online calculator that can be used by surgeons at the point of care to enable more informed decision-making.METHODSThis study identified 5114 potentially eligible patients age ≥ 65 years who presented to our two hospitals in Holland (one is a Level 1 trauma center and one is a Level 2 trauma center) between January 2016 and December 2023. Of those, we considered 3488 patients eligible because they were ≥ 65 years of age and had a first-time PHF. Based on that, 86% (2999) were included for the analysis. A further 10% (334) were excluded because of misdiagnosis, bilateral PHFs, or a history of previous PHFs. Finally, 4% (155) had an irretrievable mortality status or had incomplete data sets. Data on 24 potential factors associated with increased mortality after PHFs were collected. Surgical or nonoperative treatment were not included as the aim was to predict 1-year mortality at the moment a PHF was sustained, before a treatment choice had been made. Therefore, excluding treatment modalities does not limit the intended use as a pretreatment risk estimation model. Four ML algorithms were developed: logistic regression, extreme gradient boosting machine (XGBoost), random forest, and LightGBM. The ML algorithms were trained and internally validated on patients from the first hospital (59% [1768 of 2999]) and externally validated on a geographically distinct group of patients from the second hospital (41% [1231 of 2999]). The mean ± SD age in the training cohort was 77 ± 8 years, and it was 76 ± 8 years in the external validation set; 79% (2383 of 2999) of patients were female. The overall 1-year mortality rate was 11% (325 of 2999). Performance was assessed with discrimination and calibration curves, and overall performance was assessed using the Brier score. Discrimination was assessed with the c-statistic: the area under the receiver operating characteristic curve. The c-statistic ranges from 0.50 to 1.0, with 1.0 indicating perfect discriminating ability. Calibration was assessed by plotting the agreement between the observed outcome and predicted probability, and the intercept and slope were determined. The plot's intercept indicates whether predictions were too high (intercept < 0) or too low (intercept > 0). The slope reflects either overfitting (predictions to","PeriodicalId":10404,"journal":{"name":"Clinical Orthopaedics and Related Research®","volume":"39 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1097/corr.0000000000003815
Neel Vallurupalli,Benjamin Padon,Jie J Yao
{"title":"CORR Synthesis: How Should PROM Thresholds Be Determined and Interpreted to Reflect Clinically Meaningful Change in Orthopaedic Surgery?","authors":"Neel Vallurupalli,Benjamin Padon,Jie J Yao","doi":"10.1097/corr.0000000000003815","DOIUrl":"https://doi.org/10.1097/corr.0000000000003815","url":null,"abstract":"","PeriodicalId":10404,"journal":{"name":"Clinical Orthopaedics and Related Research®","volume":"95 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-15DOI: 10.1097/CORR.0000000000003830
Joseph Bernstein
{"title":"Letter to the Editor: Editorial: Fully Compromised, but Thanks All the Same to Our Peer Reviewers.","authors":"Joseph Bernstein","doi":"10.1097/CORR.0000000000003830","DOIUrl":"https://doi.org/10.1097/CORR.0000000000003830","url":null,"abstract":"","PeriodicalId":10404,"journal":{"name":"Clinical Orthopaedics and Related Research®","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146050661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 10.1097/CORR.0000000000003823
John D Kelly
{"title":"Your Best Life: Care for Your Brain and Put Your Phone Away!","authors":"John D Kelly","doi":"10.1097/CORR.0000000000003823","DOIUrl":"https://doi.org/10.1097/CORR.0000000000003823","url":null,"abstract":"","PeriodicalId":10404,"journal":{"name":"Clinical Orthopaedics and Related Research®","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146050642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1097/corr.0000000000003818
Alan David Lam,Jessica H Leipman,Samantha S Meacock,Nihir Parikh,Matthew B Sherman,Yale A Fillingham,Chad A Krueger
BACKGROUNDAlthough deficits in social determinants of health (SDOH) have been previously associated with adverse clinical outcomes after primary THA and TKA, their role in the perioperative communication workload remains poorly characterized. Even though it remains essential to appropriately identify and address modifiable SDOH before a procedure, orthopaedic practices must also have the resources to handle the coordination of care effectively. Understanding how deficiencies in SDOH can impact communication workload would help support effective resource planning and equitable patient engagement strategies, particularly as more perioperative management takes place outside the hospital setting.QUESTIONS/PURPOSES(1) What are the differences in touchpoint utilization in patients who live in locations with varying Area Deprivation Index (ADI) scores, a surrogate measure for social deprivation? (2) Does social deprivation have an association with the length of stay (LOS) during primary total joint arthroplasty? (3) How are readmission rates and patient-reported outcome measures (PROMs) different in patients living in areas with varying degrees of social deprivation?METHODSIn this retrospective, comparative study, there were 92,801 patients who underwent primary, elective THA (43% [39,963]) or TKA (57% [52,837]) for osteoarthritis at one high-volume, urban, academic institution between January 2016 and December 2022. Of those, exclusions consisted of indications other than osteoarthritis (2% [1595]), no available ADI data (13% [12,302]), or loss to minimum 90-day postoperative follow-up and incomplete data (29% [26,836]). In all, 52,068 patients were included in the final analysis, with 43% (22,363) of patients undergoing primary THA and 57% (29,705) undergoing primary TKA. To determine the degree of social deprivation, the 2022 ADI was used and linked to patients' street addresses. Using the ADI national ranking from 1 to 100, with 1 representing the lowest level of disadvantage and 100 representing the highest level of disadvantage, patients were compared by ADI quartiles; Quartile 1 represented the least disadvantaged cohort and Quartile 4 represented the most disadvantaged cohort. Overall, the mean ± SD age was 66 ± 10 years, and the population consisted of 56% (29,333 of 52,068) women. Thirty-three percent (17,391 of 52,068) of patients were in ADI Quartile 1, 44% (22,944 of 52,068) were in Quartile 2, 17% (8650 of 52,068) were in Quartile 3, and 6% (3083 of 52,068) were in Quartile 4. The primary outcome measure was the number of touchpoints per patient, defined as the communication points (telephone or electronic messages) sent or received on behalf of the patient in relation to the total joint arthroplasty procedure. Touchpoints within the 30-day preoperative or 90-day postoperative periods of the primary THA or TKA were included. Secondary outcome measures included LOS, 90-day readmissions, and PROMs consisting of the Knee Injury and Osteoarthr
{"title":"Higher Area Deprivation Index Is Associated With Greater Practice-initiated Perioperative Communication Workload in Patients With Primary Total Joint Arthroplasty.","authors":"Alan David Lam,Jessica H Leipman,Samantha S Meacock,Nihir Parikh,Matthew B Sherman,Yale A Fillingham,Chad A Krueger","doi":"10.1097/corr.0000000000003818","DOIUrl":"https://doi.org/10.1097/corr.0000000000003818","url":null,"abstract":"BACKGROUNDAlthough deficits in social determinants of health (SDOH) have been previously associated with adverse clinical outcomes after primary THA and TKA, their role in the perioperative communication workload remains poorly characterized. Even though it remains essential to appropriately identify and address modifiable SDOH before a procedure, orthopaedic practices must also have the resources to handle the coordination of care effectively. Understanding how deficiencies in SDOH can impact communication workload would help support effective resource planning and equitable patient engagement strategies, particularly as more perioperative management takes place outside the hospital setting.QUESTIONS/PURPOSES(1) What are the differences in touchpoint utilization in patients who live in locations with varying Area Deprivation Index (ADI) scores, a surrogate measure for social deprivation? (2) Does social deprivation have an association with the length of stay (LOS) during primary total joint arthroplasty? (3) How are readmission rates and patient-reported outcome measures (PROMs) different in patients living in areas with varying degrees of social deprivation?METHODSIn this retrospective, comparative study, there were 92,801 patients who underwent primary, elective THA (43% [39,963]) or TKA (57% [52,837]) for osteoarthritis at one high-volume, urban, academic institution between January 2016 and December 2022. Of those, exclusions consisted of indications other than osteoarthritis (2% [1595]), no available ADI data (13% [12,302]), or loss to minimum 90-day postoperative follow-up and incomplete data (29% [26,836]). In all, 52,068 patients were included in the final analysis, with 43% (22,363) of patients undergoing primary THA and 57% (29,705) undergoing primary TKA. To determine the degree of social deprivation, the 2022 ADI was used and linked to patients' street addresses. Using the ADI national ranking from 1 to 100, with 1 representing the lowest level of disadvantage and 100 representing the highest level of disadvantage, patients were compared by ADI quartiles; Quartile 1 represented the least disadvantaged cohort and Quartile 4 represented the most disadvantaged cohort. Overall, the mean ± SD age was 66 ± 10 years, and the population consisted of 56% (29,333 of 52,068) women. Thirty-three percent (17,391 of 52,068) of patients were in ADI Quartile 1, 44% (22,944 of 52,068) were in Quartile 2, 17% (8650 of 52,068) were in Quartile 3, and 6% (3083 of 52,068) were in Quartile 4. The primary outcome measure was the number of touchpoints per patient, defined as the communication points (telephone or electronic messages) sent or received on behalf of the patient in relation to the total joint arthroplasty procedure. Touchpoints within the 30-day preoperative or 90-day postoperative periods of the primary THA or TKA were included. Secondary outcome measures included LOS, 90-day readmissions, and PROMs consisting of the Knee Injury and Osteoarthr","PeriodicalId":10404,"journal":{"name":"Clinical Orthopaedics and Related Research®","volume":"39 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Background: </strong>The C2-7 Cobb angle is an important parameter in evaluating cervical sagittal alignment, which is widely used for preoperative planning, identifying surgical indications, and postoperative assessment. However, this angle becomes unmeasurable in 28% to 49% of clinical radiographs because of poor visualization of the C7 inferior endplate, limiting treatment planning and radiographic follow-up in cervical alignment assessment. The C2-6 Cobb angle has been proposed as a substitute in previous research, but these studies were limited by small symptomatic cohorts from a single center and lacked both subgroup-specific and external validation. Furthermore, there is currently a lack of reference standards for the clinical use of the C2-6 Cobb angle, and no established machine-learning models are available to accurately predict the C2-7 Cobb angle.</p><p><strong>Questions/purposes: </strong>(1) Can the C2-6 Cobb angle serve as a reliable substitute for the C2-7 angle? (2) Can machine-learning models accurately predict the C2-7 Cobb angle?</p><p><strong>Methods: </strong>We conducted a retrospective, multicountry imaging study from January 2020 to January 2025, utilizing standing lateral cervical spine radiographs from a large hospital data set in China and public data sets from Vietnam and India. In China, 11,800 radiographs were initially screened. The inclusion criterion was cervical radiographs of sufficient clarity. The exclusion criterion was cervical radiographs with incomplete visualization of anatomic structures. Following these exclusions, 10,571 radiographs from China were included, comprising 10,000 standard standing lateral radiographs plus 284 implant and 287 flexion-extension radiographs. From the public data sets, 470 radiographs from Vietnam and 62 from India were reviewed, with no radiographs excluded. A total of 11,103 radiographs were available for final analysis. Key variables included demographics (age, sex), symptomatic status, implant status, and radiographic sagittal parameters derived from standing lateral views. Four orthopaedic specialists labeled keypoints on the original radiographs, including the corner points of C2 to C7 and the centroid of C2. An algorithm was employed for precise measurement of the C2-6 and C2-7 Cobb angles. The Pearson correlation coefficient was calculated to assess the strength of the correlation between the C2-6 and C2-7 Cobb angles, and a linear regression analysis was applied to derive a predictive equation for the C2-7 Cobb angle based on the C2-6 Cobb angle. Subsequently, the 10,000 standard Chinese standing lateral radiographs were randomly assigned to the training set (80%) and the testing set (20%). An independent validation set (n = 1103) was established to assess robustness, comprising 284 implant radiographs and 287 flexion-extension radiographs from China, together with 470 from Vietnam and 62 from India.</p><p><strong>Results: </strong>Correlation analysis dem
{"title":"What Substitution and Prediction Strategies Address the Challenge of an Unmeasurable C2-7 Cobb Angle?","authors":"Zerui Qin, Yu Ran, Zongshuo Sha, Lingmin Wu, Haodong Xiong, Qianzi Zhao, Zhongze Li, Jinsong Chen, Dongran Han, Yixing Liu, Jinyu Li, Jiang Chen","doi":"10.1097/CORR.0000000000003812","DOIUrl":"https://doi.org/10.1097/CORR.0000000000003812","url":null,"abstract":"<p><strong>Background: </strong>The C2-7 Cobb angle is an important parameter in evaluating cervical sagittal alignment, which is widely used for preoperative planning, identifying surgical indications, and postoperative assessment. However, this angle becomes unmeasurable in 28% to 49% of clinical radiographs because of poor visualization of the C7 inferior endplate, limiting treatment planning and radiographic follow-up in cervical alignment assessment. The C2-6 Cobb angle has been proposed as a substitute in previous research, but these studies were limited by small symptomatic cohorts from a single center and lacked both subgroup-specific and external validation. Furthermore, there is currently a lack of reference standards for the clinical use of the C2-6 Cobb angle, and no established machine-learning models are available to accurately predict the C2-7 Cobb angle.</p><p><strong>Questions/purposes: </strong>(1) Can the C2-6 Cobb angle serve as a reliable substitute for the C2-7 angle? (2) Can machine-learning models accurately predict the C2-7 Cobb angle?</p><p><strong>Methods: </strong>We conducted a retrospective, multicountry imaging study from January 2020 to January 2025, utilizing standing lateral cervical spine radiographs from a large hospital data set in China and public data sets from Vietnam and India. In China, 11,800 radiographs were initially screened. The inclusion criterion was cervical radiographs of sufficient clarity. The exclusion criterion was cervical radiographs with incomplete visualization of anatomic structures. Following these exclusions, 10,571 radiographs from China were included, comprising 10,000 standard standing lateral radiographs plus 284 implant and 287 flexion-extension radiographs. From the public data sets, 470 radiographs from Vietnam and 62 from India were reviewed, with no radiographs excluded. A total of 11,103 radiographs were available for final analysis. Key variables included demographics (age, sex), symptomatic status, implant status, and radiographic sagittal parameters derived from standing lateral views. Four orthopaedic specialists labeled keypoints on the original radiographs, including the corner points of C2 to C7 and the centroid of C2. An algorithm was employed for precise measurement of the C2-6 and C2-7 Cobb angles. The Pearson correlation coefficient was calculated to assess the strength of the correlation between the C2-6 and C2-7 Cobb angles, and a linear regression analysis was applied to derive a predictive equation for the C2-7 Cobb angle based on the C2-6 Cobb angle. Subsequently, the 10,000 standard Chinese standing lateral radiographs were randomly assigned to the training set (80%) and the testing set (20%). An independent validation set (n = 1103) was established to assess robustness, comprising 284 implant radiographs and 287 flexion-extension radiographs from China, together with 470 from Vietnam and 62 from India.</p><p><strong>Results: </strong>Correlation analysis dem","PeriodicalId":10404,"journal":{"name":"Clinical Orthopaedics and Related Research®","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146050583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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