Prophylactic salpingectomy for the prevention of ovarian cancer: Who should we target?
Bibliographic record
Abstract
Ovarian cancer is the most fatal gynecologic malignancy (50% 5-year survival) due to a typically advanced stage at diagnosis and a high rate of recurrence. Chemoprevention options are limited, and few interventions have been shown to reduce cancer risk or mortality. Emerging data support the model that fallopian tubes are the site of origin for a proportion of high-grade serous cancers. This implies that a subset of cancers may be prevented by removing the fallopian tubes while leaving the ovaries intact. Accordingly, there has been shift in clinical practice for average risk women; some now recommend removal of both the fallopian tubes only instead of tubal ligation for sterilization or at the time of benign gynecologic surgery. This has been termed opportunistic salpingectomy and represents a means of decreasing the burden of ovarian cancer by preventing cancers that arise in the fallopian tubes. There have been no detailed, prospective reports that have estimated ovarian cancer risk reduction with opportunistic salpingectomy, neither among women at baseline population risk nor among women at a high risk of developing the disease. The situation is complicated for women with a BRCA mutation-bilateral salpingo-oophorectomy is a proven means of risk reduction and salpingectomy alone is not the standard of care. Based on the existing data, salpingectomy alone should only be reserved for women with a lifetime risk of ovarian cancer of less than 5%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".