Outcomes of Accelerated 3-Year MD Graduates at NYU Grossman School of Medicine During Medical School and Early Residency.

Purpose: For accelerated 3-year MD (3YMD) pathways to be fully adopted in medical education, a comprehensive analysis of outcome data is needed. This study includes 7 accelerated 3YMD graduating classes at NYU Grossman School of Medicine (NYUGSOM) and reports on outcomes from both medical school and internship compared to their 4-year MD (4YMD) counterparts.

Method: Outcomes across the undergraduate-graduate medical education continuum for the first 7 classes of NYUGSOM graduates (matriculated from 2013-2019) from the accelerated 3YMD (n = 136) and 4YMD pathways (n = 681) were compared.

Learner Assessment and Program Evaluation: Supporting Precision Education.

Precision education (PE) systematically leverages data and advanced analytics to inform educational interventions that, in turn, promote meaningful learner outcomes. PE does this by incorporating analytic results back into the education continuum through continuous feedback cycles. These data-informed sequences of planning, learning, assessing, and adjusting foster competence and adaptive expertise.

A New Tool for Holistic Residency Application Review: Using Natural Language Processing of Applicant Experiences to Predict Interview Invitation.

Problem: Reviewing residency application narrative components is time intensive and has contributed to nearly half of applications not receiving holistic review. The authors developed a natural language processing (NLP)-based tool to automate review of applicants' narrative experience entries and predict interview invitation.

Approach: Experience entries (n = 188,500) were extracted from 6,403 residency applications across 3 application cycles (2017-2019) at 1 internal medicine program, combined at the applicant level, and paired with the interview invitation decision (n = 1,224 invitations).

Artificial Intelligence Screening of Medical School Applications: Development and Validation of a Machine-Learning Algorithm.

Purpose: To explore whether a machine-learning algorithm could accurately perform the initial screening of medical school applications.

Method: Using application data and faculty screening outcomes from the 2013 to 2017 application cycles (n = 14,555 applications), the authors created a virtual faculty screener algorithm. A retrospective validation using 2,910 applications from the 2013 to 2017 cycles and a prospective validation using 2,715 applications during the 2018 application cycle were performed.

Development and Validation of a Machine Learning Model for Automated Assessment of Resident Clinical Reasoning Documentation.

Background: Residents receive infrequent feedback on their clinical reasoning (CR) documentation. While machine learning (ML) and natural language processing (NLP) have been used to assess CR documentation in standardized cases, no studies have described similar use in the clinical environment.

Objective: The authors developed and validated using Kane's framework a ML model for automated assessment of CR documentation quality in residents' admission notes.

Sustainable Functionalization of PAN to Improve Tinctorial Capacity.

This study may open a new way to obtain the coloration of a polymer during functionalization. Two polyacrylonitrile (PAN) polymers in the form of textile fibers ( and ) were subjected to functionalization treatments in order to improve the dyeing capacity. The functionalizations determined by an organo-hypervalent iodine reagent developed in situ led to fiber coloration without using dyes.

Development and Validation of a Machine Learning-Based Decision Support Tool for Residency Applicant Screening and Review.

Purpose: Residency programs face overwhelming numbers of residency applications, limiting holistic review. Artificial intelligence techniques have been proposed to address this challenge but have not been created. Here, a multidisciplinary team sought to develop and validate a machine learning (ML)-based decision support tool (DST) for residency applicant screening and review.

Development of a Clinical Reasoning Documentation Assessment Tool for Resident and Fellow Admission Notes: a Shared Mental Model for Feedback.

Background: Residents and fellows receive little feedback on their clinical reasoning documentation. Barriers include lack of a shared mental model and variability in the reliability and validity of existing assessment tools. Of the existing tools, the IDEA assessment tool includes a robust assessment of clinical reasoning documentation focusing on four elements (interpretive summary, differential diagnosis, explanation of reasoning for lead and alternative diagnoses) but lacks descriptive anchors threatening its reliability.

Medical student in the family health strategy on the first years of college: perception of graduates.

There is a lack of knowledge about the effective value of the experience gained by medical students who participate in the Family Health Strategy (Estratégia Saúde da Família (ESF)) during the early stages of their medical training. This teaching strategy is based on learning by experiencing the problems that exist in real life. This study proposed to understand the value of this teaching strategy from the viewpoint of the students who had participated, after their graduation.

Deceased-donor kidney transplantation: improvement in long-term survival.

Background: Despite marked improvement in short-term renal allograft survival rates (GSR) in recent years, improvement in long-term GSR remained elusive.

Methods: We analysed the kidney transplant experience at our centre accrued over four decades to evaluate how short-term and long-term GSR had changed and to identify risk factors affecting graft survival. The study included 1476 adult recipients of a deceased-donor kidney transplant who were transplanted between 1963 and 2006 and who had received one of five distinct immunosuppressive protocols.