Gynecologic surgery for benign disease: Preserving reproductive potential
Bibliographic record
Abstract
Preserving fertility is essential when managing benign gynecologic disorders in reproductive-aged women. Surgical interventions can impact future fertility, therefore requiring an evidence-based, individualized approach. The FIGO Committee on Reproductive Endocrinology and Infertility reviewed current literature to develop recommendations for fertility-sparing surgical management. Effective treatment must balance disease control with fertility preservation. Evidence supports refined surgical techniques and alternative methods that minimize reproductive harm. In pregnancy loss management, medical treatment is preferred; however, if surgery is needed, hysteroscopic evacuation is safer than sharp curettage, reducing the risk of intrauterine adhesions. Similarly for other operative hysteroscopic procedures or abdominal procedures, including myomectomy, the use of an adhesion-reducing substance is recommended. During abdominal myomectomy, only symptomatic or fertility-impairing fibroids should be removed, as excessive resection may reduce pregnancy chances. In endometrioma surgery, preserving ovarian tissue is crucial by minimizing stripping of the pseudocapsule, or using sclerotherapy may help. Oocyte cryopreservation should be discussed when recurrence or reduced ovarian reserve is a concern. Hydrosalpinx often requires salpingectomy to enhance in vitro fertilization outcomes, but surgeons should protect ovarian blood flow by minimizing thermal injury. For adenomyosis, medical management is preferred due to the risks associated with surgical techniques like the triple-flap procedure. In polycystic ovary syndrome, laparoscopic ovarian drilling is discouraged; ovulation induction with letrozole or gonadotropins is safer and effective. In summary, fertility-sparing treatment demands personalized, evidence-based strategies that prioritize reproductive potential while managing disease. As reproductive goals evolve, continued refinement of these approaches remains vital to women's health care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".