Gender Bias in the Evaluation of Surgical Performance
Bibliographic record
Abstract
OBJECTIVE: The study aims to determine the influence of trainee gender on assessments of coronary anastomosis performance. SUMMARY OF BACKGROUND DATA: Understanding the impact of gender bias on the evaluation of trainees may enable us to identify and utilize assessment tools that are less susceptible to potential bias. METHODS: Cardiothoracic surgeons were randomized to review the video performance of trainees who were described by either male or female pronouns. All participants viewed the same video of a coronary anastomosis and were asked to grade technique using either a Checklist or Global Rating Scale (GRS). Effect of trainee gender on scores by respondent demographic was evaluated using regression analyses. Inter-rater reliability was assessed using the Cronbach's alpha. RESULTS: 103 cardiothoracic surgeons completed the Checklist (trainee gender: male n=50, female n=53) and 112 completed the GRS (trainee gender: male n=56, female n=56). For the Checklist, male cardiothoracic surgeons who were in practice <10 years ( P = 0.036) and involved in training residents ( P = 0.049) were more likely to score male trainees higher than female trainees. The GRS demonstrated high inter-rater reliability across male and female trainees by years and scope of practice for the respondent (alpha >0.900) when compared to the Checklist assessment tool. CONCLUSIONS: Early career male surgeons may exhibit gender bias against women when evaluating trainee performance of coronary anastomoses. The GRS demonstrates higher interrater reliability and robustness against gender bias in the assessment of technical performance than the Checklist, and such scales should be emphasized in educational evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".