Effects of a Resident's Reputation on Laparoscopic Skills Assessment
Bibliographic record
Abstract
OBJECTIVE: To quantify the effect of a resident's reputation on the assessment of their laparoscopic skills. METHODS: Faculty gynecologists were randomized to receive one of three hypothetical resident scenarios: a resident with high, average, or low surgical skills. All participants were then asked to view the same video of a resident performing a laparoscopic salpingo-oophorectomy that differed only by the resident description and provide an assessment using a modified OSATS (Objective Structured Assessment of Technical Skills) and a global assessment scale. RESULTS: From September 6, 2020, to October 20, 2020, a total of 43 faculty gynecologic surgeons were recruited to complete the study. Assessment scores on the modified OSATS (out of 20) and global assessment (out of 5) differed significantly according to resident description, where the high-performing resident scored highest (median scores of 15 and 4, respectively), followed by the average-performing resident (13 and 3), and finally, the low-performing resident (11 and 3) (P=.008 and .043, respectively). CONCLUSION: Faculty assessment of residents in gynecologic surgery is influenced by the assessor's knowledge of the resident's past performance. This knowledge introduces bias that artificially increases scores given to those residents with favorable reputations and decreases scores given to those with reputed surgical skill deficits. These data quantify the effect of such bias in the assessment of residents in the workplace and serve as an impetus to explore systems-level interventions to mitigate bias.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".