Accuracy of Surgeon Self-Reflection on Hysterectomy Quality Metrics
Bibliographic record
Abstract
OBJECTIVE: To evaluate the accuracy of gynecologic surgeons' self-reflection across hysterectomy case volume, proportion of cases performed using a minimally invasive approach (minimally invasive rate), and complication rate and to assess whether accuracy is associated with specific surgeon or practice characteristics. METHODS: This was a cross-sectional cohort study of gynecologic surgeons at eight Canadian hospitals between 2016 and 2019. Surgeons estimated case volume, minimally invasive rate, and complication rate for hysterectomies for a 6-month period using an online survey. Kendall's tau-beta correlation coefficient (τ) measured association between estimated and actual performance. Differences (delta) between each surgeon's estimated and actual performance were calculated. The central tendency of differences among the cohort was represented by a median (median delta) and compared with 0 (perfect accuracy) using the Wilcoxon signed rank test. Differences in characteristics between surgeons classified as underestimators, accurate estimators, and overestimators by tertile of delta were evaluated using analysis of variance and χ2 tests. RESULTS: Eighty-four surgeons across eight hospitals were included. Association between estimated and actual performance was moderate for case volume (τ=0.46, P<.001) and minimally invasive rate (τ=0.52, P<.001) and weak for complication rate (τ=0.14, P=.080). Surgeons underestimated their complication rate (median delta -7.0%, 95% CI -11.0% to -3.5%, P<.001) but accurately estimated case volume (median delta 1.0, 95% CI 0.0-2.5, P=.082) and minimally invasive rate (median delta 4.0%, 95% CI -4.5% to 10.0%, P=.337). Surgeons who underestimated their complication rates had higher average complication rates (33.7%) than those who estimated accurately (12.1%, P<.001) or overestimated (7.7%, P<.001) and were more likely to be fellowship-trained (P<.001). CONCLUSION: Attending gynecologic surgeons inaccurately reflect on their complication rates, and those who most underestimate their complication rates have higher rates than their peers.
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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 | 0.016 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".