Effect of Autologous Platelet-Rich Plasma Therapy on the Pregnancy Outcomes of Women with Repeated Implantation Failure: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: A major challenge in reproductive medicine is repeated implantation failure (RIF). Possible benefits of platelet-rich plasma (PRP) for pregnancy outcomes are still uncertain, and more evidence is required to properly evaluate this. The current meta-analysis was therefore carried out to assess the impact of intrauterine PRP infusion on pregnancy outcomes in women with RIF. Methods: Various databases (Web of Science, PubMed, Cochrane Library, Embase) were screened for English-language papers that investigated the effect of PRP treatment on pregnancy outcomes in RIF women who underwent in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI). This effect was analyzed in both frozen-thawed and fresh cycles. These studies involved randomized controlled trial (RCT) and quasi-experimental (non-randomized experimental) studies, but excluded case-control, case series, self-control, cross-sectional studies. The Newcastle-Ottawa Scale was employed to determine study quality. Risk ratios (RRs) were calculated for dichotomous outcome variables, and weighted mean difference (WMD) with 95% confidence interval (95% CI) for continuous outcome variables. These were performed under fixed- or random-effect models. Results: This meta-analysis evaluated 15 articles from the literature. Improved pregnancy outcomes were observed in RIF women who received PRP, including higher rates of implantation, clinical pregnancy and live birth compared to control patients. Conclusions: The results of this study indicate that PRP could be a useful treatment strategy for RIF patients and those with a thin endometrium. Additional large RCTs are required to identify the subpopulation of women who could derive the maximum benefit from PRP.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".