Regional variation of hysterectomy for benign uterine diseases in Switzerland
Bibliographic record
Abstract
BACKGROUND: Hysterectomy is the last treatment option for benign uterine diseases, and vaginal hysterectomy is preferred over more invasive techniques. We assessed the regional variation in hysterectomy rates for benign uterine diseases across Switzerland and explored potential determinants of variation. METHODS: We conducted a population-based analysis using patient discharge data from all Swiss hospitals between 2013 and 2016. Hospital service areas (HSAs) for hysterectomies were derived by analyzing patient flows. We calculated age-standardized mean procedure rates and measures of regional variation (extremal quotient [EQ], highest divided by lowest rate) and systematic component of variation [SCV]). We estimated the reduction in the variance of crude hysterectomy rates across HSAs in multilevel regression models, with incremental adjustment for procedure year, age, cultural/socioeconomic factors, burden of disease, and density of gynecologists. RESULTS: Overall, 40,211 hysterectomies from 54 HSAs were analyzed. The mean age-standardized hysterectomy rate was 298/100,000 women (range 186-456). While the variation in overall procedure rate was moderate (EQ 2.5, SCV 3.7), we found a very high procedure-specific variation (EQ vaginal 5.0, laparoscopic 6.3, abdominal 8.0; SCV vaginal 17.5, laparoscopic 11.2, abdominal 16.9). Adjusted for procedure year, demographic, cultural, and sociodemographic factors, a large share (64%) of the variance remained unexplained (vaginal 63%, laparoscopic 85%, abdominal 70%). The main determinants of variation were socioeconomic/cultural factors. Burden of disease and the density of gynecologists was not associated with procedure rates. CONCLUSIONS: Switzerland has a very high regional variation in vaginal, laparoscopic, and abdominal hysterectomy for benign uterine disease. After adjustment for potential determinants of variation including demographic factors, socioeconomic and cultural factors, burden of disease, and the density of gynecologists, two thirds of the variation remain unexplained.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".