The Impact of Noncavity-Distorting Intramural Fibroids on Live Birth Rate in <i>In Vitro</i> Fertilization Cycles: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
fertilization (IVF) cycles? We searched Embase, MEDLINE, Google Scholar, Cochrane Library, and PUBMED from inception to May 2018. We included studies with women undergoing IVF treatment who had at least one noncavity-distorting intramural fibroid. The studies had to report one or more of the following outcomes: live birth rate as our primary outcome, and implantation rate, clinical pregnancy rate, or miscarriage rate as our secondary outcomes. We excluded studies where women also had submucosal fibroids or had undergone myomectomy. Two authors independently selected studies and extracted data. Methodological quality was assessed using Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. We included 15 studies with 5029 patients. Patients with noncavity-distorting intramural fibroids had 44% lower odds of live birth (estimated average odds ratio [OR] = 0.56, 95% confidence interval [CI] = 0.46-0.69) and 32% lower odds of clinical pregnancy (estimated average OR = 0.68, 95% CI = 0.56-0.83). Subgroup analysis of women with purely intramural fibroids showed significantly lower odds of live birth rates and clinic pregnancy rates. Analysis of prospective and retrospective studies shows that noncavity-distorting intramural fibroids have a significant adverse effect on live birth rates in women undergoing IVF. Further, well-designed prospective studies are needed to investigate whether removal of these fibroids improves IVF outcomes in this population.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".