Opportunistic salpingectomy between 2011 and 2016: a descriptive analysis
Bibliographic record
Abstract
BACKGROUND: Opportunistic salpingectomy (OS) is the removal of fallopian tubes during hysterectomy for benign indications or instead of tubal ligation, for the purpose of preventing ovarian cancer. We determined rates of OS at the time of hysterectomy and tubal sterilization and examined how they changed over the study period. METHODS: Using data from the Canadian Institute for Health Information's Discharge Abstract Database and National Ambulatory Care Reporting System for all Canadian provinces and territories (except Quebec) between the fiscal years 2011 and 2016, we conducted a descriptive analysis of all patients aged 15 years or older who underwent hysterectomy or tubal sterilization. We excluded those with diagnostic codes for any gynecologic cancer and those who underwent unilateral salpingectomy. We examined the proportion who had OS during their hysterectomy and compared the proportion of tubal sterilizations that were OS with the proportion that were tubal ligations. RESULTS: A total of 318 528 participants were included in the study (mean age 42.5 yr). The proportion of hysterectomies that included OS increased from 15.4% in 2011 to 35.5% by 2016. With respect to tubal sterilization, the rate of OS increased from 6.5% of all tubal sterilizations in 2011 to 22.0% in 2016. There was considerable variation across jurisdictions in 2016, with British Columbia having the highest rates (53.2% of all hysterectomies and 74.0% of tubal sterilizations involved OS). INTERPRETATION: The rates of OS increased between 2011 and 2016, but there was considerable variation across the included jurisdictions. Our study indicates room for rates of OS to increase across many of the included jurisdictions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".