Reproductive outcomes of women with moderate to severe intrauterine adhesions after transcervical resection of adhesion: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Intrauterine adhesions (IUA) refers to the adhesion of the inner wall of the uterus, resulting in complete or partial occlusion of the uterine cavity, which causes a series of symptoms. Transcervical resection of adhesion (TCRA) is the standard surgical method for patients with IUA. However, the recurrence rate of women with moderate to severe IUA is high and it has raised a big concern about the reproductive outcomes. METHODS: We conducted a scoping review by using 4 databases, including Google Scholar, PubMed, Scopus, Embase, and web of science, to retrieve relevant literature from September 1, 2001, to February 1, 2023, and to explore the reproductive outcomes in women with moderate to severe IUA after TCRA treatment. Following defined guidelines, data extraction was carried out by 2 researchers, and the findings were examined by 2 senior academics. The papers were evaluated by 2 reviewers using the inclusion and exclusion criteria. Using a form developed especially for this study, pertinent information was retrieved, including the first author, research design, study duration, age, intervention measurement, pregnancy rate, techniques of conception, and live birth rate. Two researchers conducted a quality assessment to determine any potential bias using the Cochrane technique and the Newcastle-Ottawa scale. RevMan 5.4.1 (The Cochrane Collaboration, London, United Kingdom) was used for data analysis, while I2 was used to evaluate heterogeneity. RESULTS: In total, this study included 2099 participants. After a detailed systematic review and meta-analyses, the results showed that pregnancy and live birth rates were increased significantly after TCRA, and the risk difference of the pregnancy rate was 1.75 [1.17, 2.62]. Besides, in 2 retrospective studies, the risk difference of live birth rate was 2.26, with a 95% confidence interval of 1.99 to 2.58. Moreover, the menstrual status of women also was improved, and the risk difference of hypermenorrhoea and amenorrhea were -0.28 [-0.37, -0.19] and -0.06 [0.26, 0.13], respectively. CONCLUSIONS: Taken together, TCRA is the useful strategy for the treatment of moderate to severe IUA to enhance the reproductive outcomes in women.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.003 | 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".