Exploring opportunistic salpingectomy as a preventive strategy for ovarian cancer: uptake in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Ovarian cancer (OC), the fifth deadliest cancer for women, had an estimated 3,100 cases in Canada in 2023, with a 66% mortality rate and approximately 1,950 projected deaths. High-grade serous carcinoma (HGSC), comprising 75% of OC cases, presents challenges in screening and early detection. Advances in understanding the origins of HGSC led to the identification of serous tubal intraepithelial carcinomas (STICs) in the fallopian tubes, introducing opportunistic salpingectomy (OS) as a preventative measure. The feasibility, safety, cost-effectiveness, and efficacy of OS have been investigated in multiple studies. A narrative review was conducted to address all these elements by assessing the effect of OS on surgical and post-surgical complications and on ovarian reserve. Overall, the addition of OS to hysterectomy or instead of tubal ligation appears to be safe and feasible. Available retrospective studies demonstrated that OS reduces the risk of OC in the general population by 35% to 65%. Due to the novelty of this approach, our understanding of the uptake of OS is limited in different clinical settings. The second manuscript presented in this thesis is a quantitative retrospective study that assessed the uptake of OS in Newfoundland and Labrador (NL) between 2010 and 2019. All patients who underwent any or any combination of hysterectomy, salpingectomy, oophorectomy, or tubal ligation were included in the analysis. The number of cases with gynecological cancers following the surgery was also reported for each group. Over the study period, the uptake of OS at the time of hysterectomy and as an alternative to tubal ligation increased by 10.3-fold and 28.1- fold, respectively. However, despite this upward trend, there is still room to enhance its adoption in NL, Canada.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".