The efficacy of intrauterine infusion of platelet rich plasma in women undergoing assisted reproduction: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Platelet-rich plasma (PRP) is an autologous platelet concentration recently used in the reproductive field. Studies had conflicting results regarding its effect on pregnancy outcomes. We aimed to solve the debate on the safety and efficacy of PRP in women undergoing assisted reproduction and assess the influence of covariates on the outcomes of PRP infusion. METHODS: We searched PubMed, Scopus, Cochrane, and Web of Science in May 2023. We included randomized and non-randomized clinical trials as well as cohort studies assessing intrauterine PRP in sub fertile women undergoing assisted reproduction (IVF/ICSI). For the quality assessment, We used the Cochrane Risk of Bias Tool 1, the ROBINS-I tool, and the Newcastle-Ottawa Scale. We pooled the data using RevMan version 5.4. RESULTS: The data from 23 studies were pooled. PRP had favorable outcomes compared with the control group on clinical pregnancy rate (RR: 1.84, 95% CI 1.62 to 2.09; P < 0.00001), live birth rate (RR: 1.75, 95% CI: 1.24 to 2.47; P = 0.001), and miscarriages (RR: 0.51, 95% CI: 0.36 to 0.72; P = 0.0002). Women with repeated implantation failure had a significantly improved clinical pregnancy rate (RR: 1.83, 95% CI: 1.49 to 2.24; P < 0.00001), live birth rate (RR:1.83, 95% CI: 1.33 to 2.51; P = 0.002), and miscarriage rate (RR: 0.46, 95% CI: 0.31 to 068; P = 0.0001). CONCLUSION: PRP showed promising results in assisted reproductive techniques. Further large and multicenter RCTs are required to compare the doses of PRP while identifying the specific population with the most benefits 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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| 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".