Effect of dual trigger on reproductive outcome in low responders: a systematic PRISMA review and meta-analysis
Bibliographic record
Abstract
Objective Poor ovarian responders (PORs) pose a great challenge for fertility clinics worldwide. The aim of this study was to examine whether ‘dual trigger’ consisting of human chorionic gonadotropin (hCG) plus gonadotropin-releasing hormone agonist (GnRHa) is beneficial or not regarding implantation rate, pregnancy rate, and live birth rate for POR.Methods This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Risk of bias was evaluated by the Newcastle–Ottawa scale or version 2 (NOS) of the Cochrane risk-of-bias tool for randomized trials (ROB2) independently by two authors. Furthermore, RevMan version 5.4 was used to analyze the extracted data and to create an inverse-weighted summary-odds ratio (OR).Results A total of 1390 studies were screened. Seven studies containing a total of 2474 POR were included. The pooled meta-analysis revealed a 1.62-fold increase in clinical pregnancy rate (OR = 1.62 [1.00, 2.62], p = .05) and a 2.65-fold increase in live birth rate (OR = 2.65 [1.66, 4.24], p < .0001) in the dual trigger group compared to hCG trigger. The pooled analysis showed no significant difference between the two groups regarding implantation rate (OR = 1.14 [0.93, 1.39], p = .21).Conclusions The meta-analysis of this study indicates that dual trigger as finale oocyte maturation is advantageous compared to hCG trigger among POR. However, large-scale, high-quality, randomized controlled trials (RCT) are required to confirm this conclusion and fully address the magnitude of this effect.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.045 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".