Correlation between the times of IUI and pregnancy outcome: a systematic review and meta-analysis with trial sequential analysis of cohort study
Bibliographic record
Abstract
Abstract Purpose the study aims to evaluate whether increasing the times of IUI per cycle has better pregnancy outcomes for infertile couples. Methods PubMed, Embase, Web of science, Cochrane Library, CNKI, WanFang Data, VIP (VIP Chinese Journal Service Platform), and SinoMed (China Biomedical Literature Service), were systematically retrieved cohort studies about the times of IUI per cycle and pregnancy outcomes published before December 31, 2021. Included studies were assessed by Newcastle-Ottawa Scale (NOS) with two reviewers. The data was processed with RevMan 5.4.1 and the result of the meta-analysis were further examined by TSA 0.9.5.10 Beta version. Results 23 cohort studies with 54208 IUI cycles were included in this study. Meta-analysis of the data reported in these 23 studies showed that the cycle pregnancy rates were higher in the double insemination group (double IUI) than in the single insemination group (single IUI) [RR = 1.14, 95% CI (1.02, 1.27), I2 = 64%, P = 0.02]. Sensitivity analysis showed that the results were stable and reliable. The bias analysis showed that there was a low possibility of publication bias in this study. TSA of 23 trials illustrate that the cumulative z-curve crossed the traditional boundary, the trial sequential monitoring boundary and the required information size(RIS). Conclusion compared with single IUI, double IUI per cycle can increase the cycle pregnancy rates to a small extent without increasing the incidence of biochemical pregnancy, miscarriage, or ectopic pregnancy. The results should be treated with caution and other factors should be taken into consideration when making clinical decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.051 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".