Which patients with hydrosalpinx will benefit more from reproductive surgery to improve natural pregnancy outcomes?: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The most common tubal disease leading to infertility occurs in the distal region, manifesting as hydrosalpinx. Tubal surgery is an effective alternative treatment. However, subpopulations that benefit the most from tubal repair surgery remain unclear. The objective of this study was to investigate the natural pregnancy outcomes of patients with hydrosalpinx after reproductive surgery and those with different grades of hydrosalpinx. METHODS: We searched the major online databases (PubMed, Embase, Cochrane Library, Web of Science, and Clinical Trials) to collect observational studies on patients with hydrosalpinx who underwent surgeries to preserve natural fertility from January 2000 to August 2022. The outcome indicators were natural intrauterine pregnancy (IUP) and ectopic pregnancy (EP) rates. Studies on patients with hydrosalpinx who underwent laparoscopic surgeries and those who intended to be conceived naturally were included. Studies on patients with non-hydrosalpinx diseases, those who underwent open surgery, and those who intended to undergo assisted conception were excluded. The Newcastle-Ottawa scale for observational studies was used for quality evaluation. Meta-analysis of a single rate was performed using RevMan5.3 software. RESULTS: A total of 10 articles were included in this study, including 1317 patients with hydrosalpinx. Seven studies were retrospective and 3 were prospective. It was found that after surgery for preserving natural fertility function, the IUP and EP rates of patients with hydrosalpinx were 27% (95% confidence interval [CI]: 22-32%) and 4.8% (95% CI: 2.91-8.26%), respectively. In addition, the IUP and EP rates in patients with mild (n = 254), moderate (n = 252), and severe (n = 473) hydrosalpinx were 50.5% (95% CI: 38.65-61.97%), 32.9% (95% CI: 21.88-46.24%), 10.7% (95% CI: 4.76-21.88%), and 7.4% (95% CI: 2.91-19.35%), 9.09% (95% CI: 6.54-13.79%), 8.3%, 8.26% (95% CI: 3.85-18.03%), respectively. CONCLUSION: Patients with mild to moderate hydrosalpinx will benefit more from reproductive surgery to improve natural pregnancy outcomes. However, the small sample size in our study needs to be further expanded, and the grouping needs to be more refined, such as grouping based on age. This may provide more guidance in clinical practice.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".