Opportunistic Salpingectomy at the Time of General Surgery Procedures: A Systematic Review and Narrative Synthesis of Current Knowledge
Bibliographic record
Abstract
Opportunistic salpingectomy (OS) for the primary prevention of ovarian cancer is performed by gynecologists. Advocates have suggested expanding its use to other surgical specialties. General surgeons are the other group to routinely perform intraperitoneal operations in women and could play a role in ovarian cancer prevention. Herein, we review the current evidence and perioperative factors requiring consideration prior to OS implementation in select general surgery cases. A systematic search was conducted for English-language studies evaluating OS during general surgery. The primary outcomes of this study were the feasibility and safety of OS during general surgery procedures. Secondary outcomes included pre-operative considerations (patient selection and the consent process), operative factors (technique and surgical specialty involvement), and post-operative factors (follow-up and management of operative complications). We evaluated 3977 studies, with 9 meeting the eligibility criteria. Few studies exist but preliminary evidence suggests relative safety, with no complication attributable to OS among 140 patients. Feasibility was reported in one study, which showed the capacity to perform OS in 98 out of 105 cholecystectomies (93.3%), while another study reported quick visualization of the fallopian tubes in >80% of cases. All patients in the included studies were undergoing elective procedures, including cholecystectomy, interval appendectomy, colorectal resection, bariatric surgery, and laparoscopic hernia repair. Studies only included patients ≥ 45 years old, and the mean age ranged from 49 to 67.5 years. Gynecologists were frequently involved during the consent and surgical procedures. OS represents a potential intervention to reduce the risk of ovarian cancer. Ongoing studies evaluating the general surgeon’s understanding; the consent process; the feasibility, operative outcomes, and risks of OS; and surgeon training are required prior to consideration.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".