Efficacy of Intrauterine Infusion Platelet-Rich Plasma on the Outcomes of IVF-ICSI cycles in Women with Recurrent IVF-ICSI Failure: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: : In the context of reproductive healthcare, repetitive implantation failures present a serious chal-lenge (RIF). Since the implications are now unclear, more research is required to fully assess the probable benefits of platelet-rich plasma (PRP) for pregnancy outcomes. Thus, the current meta-analysis was carried out to assess the impact of intrauterine PRP injection on pregnancy re-sults in women with RIF. Methods: A number of databases, including Web of Sci-ence, PubMed, the Cochrane Library, and Embase, were searched for English-language studies that examined how therapy with PRP affected the success of IVF/ICSI pro-cedures in conceiving RIF women. Fresh cycles as well as frozen-thawed cycles were examined for this impact. Case-control, case series, self-control, and cross-sectional research were not included in this set of studies; instead, they focused on randomized controlled trials (RCTs) and non-randomized experimental investigations. The New-castle-Ottawa Scale was utilized to assess the quality of the studies. For dichotomy outcome indicators, risk ratios (RRs) were computed; for continuous outcome parame-ters, weighted mean difference (WMD) with 95% confi-dence interval (CI) was computed. Either fixed-effect or random-effect models were used for these. Results: Fifteen publications were assessed in the pub-lished literature for this meta-analysis. PRP-treated RIF women had better pregnancy outcomes than control pa-tients, with greater rates of implantation, clinical pregnan-cy, and live birth. Conclusion: Based on the study's findings, patients with RIF and those with poor endometrium may find PRP to be an effective therapy option. To determine the subgroup of women who could benefit from PRP the most, further extensive RCTs are needed.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".