Audit of Minimally Invasive Hysterectomy Rates: A Canadian Retrospective Cross-Sectional Database Review
Bibliographic record
Abstract
Background: Minimally invasive hysterectomy is generally preferable to abdominal hysterectomy. The technicity index (TI) is the proportion of hysterectomies performed by minimally invasive surgery. Many centers globally have started to audit local TI as a quality indicator, but only a handful have published their results to help define international standards of care. Objective: In this study, TI was examined in Winnipeg and Canada to determine consistency between local and national patterns of practice, audit expected changes, and contribute to the growing body of literature defining international standards of care. Methods: A retrospective cross-sectional database review of hysterectomies performed in the Winnipeg Regional Health Authority (WRHA) from 2008 to 2015 was conducted. Mixed effects linear regression models were generated primarily to analyze TI and account for surgeon and hospital characteristics. The Canadian Institute for Health Information (CIHI) database was accessed to estimate the average national TI from 2009 to 2014. One-sample t tests compared annual WRHA and CIHI TI. Results: In Winnipeg, 1363±32 hysterectomies were performed annually for all indications with an average TI of 34% independent of time (P=0.09). The CIHI database recorded approximately 27 000 hysterectomies annually with increasing TI (41%-52%, 3.5±1.8%/year, P=0.025). WRHA TI differed from national TI every year (P<2.2x10-16). Conclusion: Over the study period, WRHA TI was below the Canadian average and static despite national increases. The importance of local audits to identify underperformance and stimulate initiatives for quality improvement is highlighted in this study.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".