Efficacy Evaluation of Aspirin Plus Prednisone or Prednisolone in IVF/RIF Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: We conducted a comprehensive systematic review and meta-analysis to evaluate the diverse impacts of combining aspirin with prednisone or prednisolone on patients undergoing in vitro fertilization (IVF) or experiencing recurrent implantation failure (RIF). Our analysis encompassed parameters such as clinical pregnancy rate, implantation rate, live birth rate, miscarriage rate, and fertilization rate. Our primary objective was to resolve the debate regarding the comparative efficacy of prednisone versus prednisolone when administered alongside aspirin in women undergoing assisted reproduction. Methods: A variety of electronic databases were searched between 1984 and December 2023, including PubMed, Web of Science, Embase, the China National Knowledge Infrastructure (CNKI), the China Biology Medicine Disc (CBM), and the CQVIP Database. We employed the Newcastle-Ottawa Scale and the Cochrane Risk of Bias Tool for the quality evaluation. We used Stata 12.0 and Revman 5.4 to pool the data. Results: In this meta-analysis, 10 trials, including 2902 individuals, were considered. Aspirin plus prednisone or prednisolone might improve clinical pregnancy rates (relative risk (RR) = 1.13; 95% confidence interval (95% CI) = 1.03–1.23) and implantation rates (RR = 1.27; 95% CI = 1.01–1.60) compared with the placebo or no treatment group. Conclusions: Our findings suggest that aspirin plus prednisone or prednisolone may improve clinical pregnancy rates and implantation rates in patients with IVF or RIF, and in the subgroup of ANA-positive patients, it may also improve implantation rates. Further design of larger randomized controlled trials is required to determine whether aspirin in combination with prednisone or prednisolone may improve assisted reproductive outcomes in patients undergoing IVF or RIF, considering the limits of study enrollment.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.007 | 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".