Laparoscopic ovarian transposition prior to pelvic radiation in young women with anorectal malignancies: a systematic review and meta‐analysis of prevalence
Bibliographic record
Abstract
AIM: Young women undergoing radiotherapy (RT) for pelvic malignancies are at risk of developing premature ovarian insufficiency. Ovarian transposition (OT) aims to preserve ovarian function in these patients. However, its role in anorectal malignancy has yet to be firmly established. The aim of this review was to determine the effectiveness of laparoscopic OT in preserving ovarian function in premenopausal women undergoing neoadjuvant pelvic RT for anorectal malignancies. METHODS: MEDLINE, Embase and CENTRAL were systematically searched from inception through to May 2022. Articles were included if they evaluated ovarian function after OT in women with anorectal malignancies undergoing pelvic RT. The primary outcome was ovarian function preservation. The secondary outcome was 30-day postoperative morbidity following OT. RESULTS: = 43%). The 30-day postoperative morbidity rate was 1.2% (n = 1). There was heterogeneity in interventions and outcome reporting. CONCLUSIONS: Laparoscopic OT in premenopausal patients undergoing pelvic radiation for anorectal malignancies might be an effective technique at reducing ovarian exposure to RT. The meta-analyses must be interpreted within the context of clinical heterogeneity of the included studies. Further studies are required to fully investigate the outcomes of OT in patients undergoing pelvic radiation for anorectal malignancies.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".