Background: Academic medical centers greatly benefit from retaining their physicians; that ensures continuity in patient care, enhances resident education, and maintains a pool of experienced clinicians and researchers. Despite its importance, little research has been published on the retainment of academic faculty in orthopaedics. To address this gap, this study investigates the demographic trends of academic orthopaedic surgeons from 2016 to 2022. By analyzing data pertaining to gender distribution, years of practice, research productivity, and institutional rankings, we aimed to gain insights into the factors influencing faculty retainment, institution changes, and new entrants into academic orthopaedics. Methods: A retrospective cross-sectional analysis of U.S. academic orthopaedic surgeons affiliated with programs under the Accreditation Council for Graduate Medical Education (ACGME) in 2016 and 2022 was performed. Faculty present in both the 2016 and the 2022 data were classified as being “retained” in academia; those present only in 2016, as having “left” academia; and those present only in 2022, as being “new” to academia. The retained group was then divided into movers (those who moved to other institutions) and non-movers. Results: Retained orthopaedists had fewer years of practice, a higher h-index (Hirsch index), and more publications. Non-fellowship-trained orthopaedists had less retainment in academia, and orthopaedists with fellowships in oncology had more retainment in academia. Additionally, movers also had fewer years in practice but an equal level of scholarly productivity when compared with non-movers. Lastly, higher-ranked academic programs retained a greater proportion of orthopaedic surgeons. Conclusions: Over the study period, a majority of orthopaedists (56.99%) chose to remain in academia. Those retained tended to be in the earlier stages of their careers, yet demonstrated higher research output. Notably, the representation of female orthopaedists in academic orthopaedics is on the rise. Conversely, lower-ranked programs faced higher turnover rates, highlighting the challenges that they encounter in retaining faculty members. Clinical Relevance: Academic medical centers benefit from retaining orthopaedic surgeons by maintaining patient relationships, having consistency in resident education, and building on clinical and research expertise. Likewise, orthopaedists benefit from understanding the trends in current academic employment, in order to optimize career planning decisions.

Introduction: The objective of this study was to search existing literature on nerve reconstruction surgery in patients with obstetric brachial plexus palsy to determine whether treatment with supraclavicular exploration and nerve grafting produced better elbow flexion outcomes compared to intercostal nerve transfer. Methods: This study was a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Individual Patient Data guidelines. A systematic search was conducted using multiple databases. An ordinal regression model was used to analyze the effect of using supraclavicular exploration and nerve grafting or intercostal nerve on elbow flexion with the two scores measured: elbow flexion Medical Research Council scores and Toronto active movements scale scores for elbow flexion. Results: A final patient database from 6 published articles consisted of 83 supraclavicular exploration and nerve grafting patients (73 patients with Medical Research Council and 10 patients with Toronto score) and 7 published articles which consisted of 131 intercostal nerve patients (84 patients with Medical Research Council and 47 patients with Toronto scores). Patients who underwent supraclavicular exploration and nerve grafting presented with an average Medical Research Council score of 3.9 ± 0.72 and an average Toronto score of 6.2 ± 2.2. Patients who underwent intercostal nerve transfer presented with an average Medical Research Council score of 3.9 ± 0.71 and an average Toronto score of 6.4 ± 1.2. There was no statistical difference between supraclavicular exploration and nerve grafting and intercostal nerve transfer when utilizing Medical Research Council elbow flexion scores (ordinal regression: 0.3821, standard error: 0.4590, p = 0.2551) or Toronto Active Movement Scale score for elbow flexion (ordinal regression: 0.7154, standard error: 0.8487, p = 0.2188). Conclusion: Regardless of surgical intervention utilized (supraclavicular exploration and nerve grafting or intercostal nerve transfers), patients had excellent outcomes for elbow flexion following obstetric brachial plexus palsy when utilizing Medical Research Council or Toronto scores for elbow flexion. The difference between these scores was not statistically significant. Type of study/Level of evidence: Therapeutic Study: Investigating the Result of Treatment/level III.

Common dysglycemia measurements including fasting plasma glucose (FPG), oral glucose tolerance test (OGTT)-derived 2 h plasma glucose, and hemoglobin A1c (HbA1c) have limitations for children. Dynamic OGTT glucose and insulin responses may better reflect underlying physiology. This analysis assessed glucose and insulin curve shapes utilizing classifications—biphasic, monophasic, or monotonically increasing—and functional principal components (FPCs) to predict future dysglycemia. The prospective cohort included 671 participants with no previous diabetes diagnosis (BMI percentile ≥ 85th, 8–18 years old); 193 returned for follow-up (median 14.5 months). Blood was collected every 30 min during the 2 h OGTT. Functional data analysis was performed on curves summarizing glucose and insulin responses. FPCs described variation in curve height (FPC1), time of peak (FPC2), and oscillation (FPC3). At baseline, both glucose and insulin FPC1 were significantly correlated with BMI percentile (Spearman correlation r = 0.22 and 0.48), triglycerides (r = 0.30 and 0.39), and HbA1c (r = 0.25 and 0.17). In longitudinal logistic regression analyses, glucose and insulin FPCs predicted future dysglycemia (AUC = 0.80) better than shape classifications (AUC = 0.69), HbA1c (AUC = 0.72), or FPG (AUC = 0.50). Further research should evaluate the utility of FPCs to predict metabolic diseases.

To evaluate a classification algorithm, it is common practice to plot the ROC curve using test data. However, the inherent randomness in the test data can undermine our confidence in the conclusions drawn from the ROC curve, necessitating uncertainty quantification. In this article, we propose an algorithm to construct confidence bands for the ROC curve, quantifying the uncertainty of classification on the test data in terms of sensitivity and specificity. The algorithm is based on a procedure called conformal prediction, which constructs individualized confidence intervals for the test set and the confidence bands for the ROC curve can be obtained by combining the individualized intervals together. Furthermore, we address both scenarios where the test data are either iid or non-iid relative to the observed data set and propose distinct algorithms for each case with valid coverage probability. The proposed method is validated through both theoretical results and numerical experiments.

Graph classification in medical imaging and drug discovery requires accuracy and robust uncertainty quantification. To address this need, we introduce Conditional Prediction ROC (CP-ROC) bands, offering uncertainty quantification for ROC curves and robustness to distributional shifts in test data. Although developed for Tensorized Graph Neural Networks (TGNNs), CP-ROC is adaptable to general Graph Neural Networks (GNNs) and other machine learning models. We establish statistically guaranteed coverage for CP-ROC under a local exchangeability condition. This addresses uncertainty challenges for ROC curves under non-iid setting, ensuring reliability when test graph distributions differ from training data. Empirically, to establish local exchangeability for TGNNs, we introduce a data-driven approach to construct local calibration sets for graphs. Comprehensive evaluations show that CP-ROC significantly improves prediction reliability across diverse tasks. This method enhances uncertainty quantification efficiency and reliability for ROC curves, proving valuable for real-world applications with non-iid objects.