Assessment of clinical teachers by learners is problematic. Construct-irrelevant factors influence ratings, and women teachers often receive lower ratings than men. However, most studies focus only on numeric scores. Therefore, the authors analyzed written comments on 4032 teacher assessments, representing 282 women and 448 men teachers in one Department of Medicine, to explore for gender differences. NVivo was used to search for 61 evidence- and theoretically-based terms purported to reflect teaching excellence, which were analyzed using 2 × 2 chi-squared tests. The Linguistic Index and Word Count (LIWC) was used to categorize comment data, which were analyzed using linear regressions. The only significant difference in NVivo was that men were more likely than women to have the word "available" in a comment (OR 1.4, p < .05). A subset of LIWC variables showed significant gender differences, but all effects were modest. Men teachers had more positive emotion words written about them, while negative emotion words appeared equally. Significant differences occurred more often between the men and women residents who wrote the comments, rather than those attributed to the gender of the teachers. For example, women residents used more social and gender-related words (β 1.87, p < 0.001) and fewer words related to power or achievement (β -3.78, p < 0.001) than men residents. Profound gender differences were not found in teacher assessment comments in this large, diverse academic department of medicine, which differs from other studies. The authors explore possible reasons including differences in departmental culture and issues related to the methods used.
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http://dx.doi.org/10.1007/s10459-021-10088-1 | DOI Listing |
Updates Surg
January 2025
Department of Surgery, Van Training and Research Hospital, University of Health Sciences, Süphan Mahallesi Hava Yolu Kavşağı 1. Kilometre Edremit, Van, Turkey.
J Am Med Inform Assoc
December 2024
Department of Radiology, Stanford University, Stanford, CA 94304, United States.
Objective: Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel preprocessed dataset, the MIMIC-IV-BHC, encapsulating clinical note and BHC pairs to adapt LLMs for BHC synthesis.
View Article and Find Full Text PDFDrug Saf
January 2025
Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Background: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear.
Objective: To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources.
Target Oncol
January 2025
College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
Background: Tumour mutational burden (TMB) is an established biomarker for patients treated with immune checkpoint inhibitors (ICIs). The optimal TMB cut-off is uncertain. It is also uncertain whether there is a sharp TMB threshold or a more graduated change in clinical outcomes as TMB increases.
View Article and Find Full Text PDFMol Diagn Ther
January 2025
Thoracic Surgery and Lung Transplantation, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, 20122, Milan, Italy.
Objectives: To investigate whether 18F-fluorodeoxyglucose positron emission tomography-computed tomography ([F]F-FDG PET/CT) metabolic parameters were associated with histology and to assess their prognostic role in patients with thymic lesions.
Patients And Methods: In total, 116 patients (49/67 M/F; mean age 59.5 years) who underwent preoperative [F]F-FDG PET/CT and thymectomy from 2012 to 2022 were retrospectively analyzed.
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