A framework to improve retention of Black surgical trainees: A Society of Black Academic Surgeons white paper.

Attrition is high among surgical trainees, and six of ten trainees consider leaving their programs, with two ultimately leaving before completion of training. Given known historically and systemically rooted biases, Black surgical trainees are at high risk of attrition during residency training. With only 4.5% of all surgical trainees identifying as Black, underrepresentation among their peers can lend to misclassification of failure to assimilate as clinical incompetence. Furthermore, the disproportionate impact of ongoing socioeconomic crisis (e.g., COVID-19 pandemic, police brutality etc.) on Black trainees and their families confers additional challenges that may exacerbate attrition rates. Thus, attrition is a significant threat to medical workforce diversity and health equity. There is urgent need for surgical programs to develop proactive approaches to address attrition and the threat to the surgical workforce. In this Society of Black Academic Surgeons (SBAS) white paper, we provide a framework that promotes an open and inclusive environment conducive to the retention of Black surgical trainees, and continued progress towards attainment of health equity for racial and ethnic minorities in the United States.

Independent assessment of a deep learning system for lymph node metastasis detection on the Augmented Reality Microscope.

Several machine learning algorithms have demonstrated high predictive capability in the identification of cancer within digitized pathology slides. The Augmented Reality Microscope (ARM) has allowed these algorithms to be seamlessly integrated within the pathology workflow by overlaying their inferences onto its microscopic field of view in real time. We present an independent assessment of the LYmph Node Assistant (LYNA) models, state-of-the-art algorithms for the identification of breast cancer metastases in lymph node biopsies, optimized for usage on the ARM. We assessed the models on 40 whole slide images at the commonly used objective magnifications of 10×, 20×, and 40×. We analyzed their performance across clinically relevant subclasses of tissue, including breast cancer, lymphocytes, histiocytes, blood, and fat. Each model obtained overall AUC values of approximately 0.98, accuracy values of approximately 0.94, and sensitivity values above 0.88 at classifying small regions of a field of view as benign or cancerous. Across tissue subclasses, the models performed most accurately on fat and blood, and least accurately on histiocytes, germinal centers, and sinus. The models also struggled with the identification of isolated tumor cells, especially at lower magnifications. After testing, we reviewed the discrepancies between model predictions and ground truth to understand the causes of error. We introduce a distinction between proper and improper ground truth for analysis in cases of uncertain annotations. Taken together, these methods comprise a novel approach for exploratory model analysis over complex anatomic pathology data in which precise ground truth is difficult to establish.

A Predictive Model for Severe COVID-19 in the Medicare Population: A Tool for Prioritizing Primary and Booster COVID-19 Vaccination.

Recommendations for prioritizing COVID-19 vaccination have focused on the elderly at higher risk for severe disease. Existing models for identifying higher-risk individuals lack the needed integration of socio-demographic and clinical risk factors. Using multivariate logistic regression and random forest modeling, we developed a predictive model of severe COVID-19 using clinical data from Medicare claims for 16 million Medicare beneficiaries and socio-economic data from the CDC Social Vulnerability Index. Predicted individual probabilities of COVID-19 hospitalization were then calculated for population risk stratification and vaccine prioritization and mapping. The leading COVID-19 hospitalization risk factors were non-white ethnicity, end-stage renal disease, advanced age, prior hospitalization, leukemia, morbid obesity, chronic kidney disease, lung cancer, chronic liver disease, pulmonary fibrosis or pulmonary hypertension, and chemotherapy. However, previously reported risk factors such as chronic obstructive pulmonary disease and diabetes conferred modest hospitalization risk. Among all social vulnerability factors, residence in a low-income zip code was the only risk factor independently predicting hospitalization. This multifactor risk model and its population risk dashboard can be used to optimize COVID-19 vaccine allocation in the higher-risk Medicare population.

The C.L.E.A.R. Score: a new comprehensive assessment method for academic physicians in military medical centers.

INTRODUCTION: Evaluation of medical officer performance is a critical leadership role. OBJECTIVES: This study offers a comprehensive evaluation system for military physicians. METHODS: The Comprehensive Assessment equation (COMPASS equation), a modified Cobb-Douglas equation, was developed to evaluate academic physicians. The COMPASS equation assesses military physicians within five comprehensive dimensions: (1) Clinical (2) Leadership, (3) Educational (4) Administrative, and (5) Research productivity excellence to yield a composite "C.L.E.A.R. Score." RESULTS: The COMPASS equation's fidelity was tested with a cohort of military physicians within the department of Obstetrics and Gynecology in the Capital District Region and a C.L.E.A.R. score was calculated for individual physicians. Mean C.L.E.A.R score was 53.6 +/- 28.8 (range 10.1-98.5). The responsiveness of the model was tested using two hypothetical physician models: "low-performing-faculty" and "super-faculty," and calculated C.L.E.A.R. scores were 6.3 and 153.4, respectively. CONCLUSIONS: The C.L.E.A.R. score appears to recognize and assess the performance excellence of military physicians. Weighting measured characteristics of the COMPASS equation can be used to promote organizational priorities. Thus, leaders of military medicine can communicate institutional priorities and inculcate them through use of the COMPASS equation to reward and recognize the activities of military medical officers that are commensurate with institutional goals.

Kaizen: a process improvement model for the business of health care and perioperative nursing professionals.

Kaizen is a proven management technique that has a practical application for health care in the context of health care reform and the 2010 Institute of Medicine landmark report on the future of nursing. Compounded productivity is the unique benefit of kaizen, and its principles are change, efficiency, performance of key essential steps, and the elimination of waste through small and continuous process improvements. The kaizen model offers specific instruction for perioperative nurses to achieve process improvement in a five-step framework that includes teamwork, personal discipline, improved morale, quality circles, and suggestions for improvement.

Primary palmoplantar hyperhidrosis and thoracoscopic sympathectomy: a new objective assessment method.

BACKGROUND: This study was conducted to establish an objective approach to evaluate symptoms and sweat production in patients with primary palmoplantar hyperhidrosis (PPH) and assess their response to bilateral thoracoscopic sympathectomy (BTS). METHODS: We conducted two institutional review board-approved studies. We performed a one-time evaluation of healthy volunteers (controls) with three questionnaires (Hyperhidrosis Disease Severity Scale, Dermatology Life Quality Index, and Short Form-36) and measurement of transepidermal water loss (TEWL; g/m(2)/h). We evaluated PPH patients with these same tools before and 1 month after BTS and compared them with controls. RESULTS: We evaluated 35 controls (mean age, 23.0 +/- 3.3 years) and 45 PPH patients (mean age, 26.5 +/- 12.3 years); 18 PPH patients underwent BTS and the 1-month postoperative evaluation. Hyperhidrosis Disease Severity Scale and Dermatology Life Quality Index scores were higher in PPH patients than in controls (p < 0.0001), but normalized after BTS. Short Form-36 scale scores were lower in PPH patients than in controls (p < 0.05), but improved significantly after BTS. Compared with controls, preoperative TEWL values were significantly higher in PPH patients (palmar: 142.7 +/- 43.6 PPH vs 115.8 +/- 48.7 controls, p = 0.011; plantar: 87.5 +/- 28.8 PPH vs 57.7 +/- 24.7 controls, p < 0.0001). After BTS, palmar TEWL values were significantly lower (49.1 +/- 29.8, p < 0.0001). Plantar TEWL did not change significantly (77.6 +/- 46.6, p = 0.52). CONCLUSIONS: PPH patients should be objectively evaluated with standardized quality of life measures and TEWL measurements before and after treatment. We believe that this objective practical approach provides a benchmark for clinical practice and research.