Day surgery for endometriosis in Canada: A retrospective cohort of trend and regional variation in types of surgeries and their complications
Bibliographic record
Abstract
BACKGROUND: A significant proportion of patients with endometriosis require surgery for management of the disease. AIMS: We aimed to assess the trend and regional variation in day surgeries for endometriosis across Canada and to identify perioperative complications associated with types of surgeries and their temporal trend and regional variation. MATERIALS AND METHODS: We conducted a retrospective cohort study of women undergoing day surgeries for endometriosis between 2015 and 2019 using Canadian administrative data from the National Ambulatory Care Reporting System, which includes data from four provinces (Ontario (ON), Alberta (AB), Nova Scotia (NS) and Prince Edward Island (PEI)). Multivariate logistic regression models were used to compare perioperative complication rates, while adjusting for site(s) of endometriosis lesion, age, type of surgical intervention, and comorbidities. RESULTS: During the study period, the rate of day surgeries for endometriosis remained nearly constant at 80-90 cases per 100 000 women of reproductive age (P = 0.12). The rate of day surgeries was significantly different between provinces (AB = 94, NS = 93 vs ON = 85 per 100 000 women of reproductive age: P < 0.02). The odds of complications decreased with time (2019 vs 2015; adjusted odds ratio (aOR): 0.84; 95% CI: 0.73-0.98). There was a significant regional variation in the frequency of perioperative complications (PEI vs ON aOR: 4.13, 95% CI: 2.58-6.62; and NS vs ON aOR: 1.47, 95% CI: 1.11-1.95). CONCLUSION: The rates of day surgery for endometriosis remained stable and the risks of perioperative complications decreased during the five-year study period. However, there were significant regional variations in the risk of perioperative complications.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".