A systematic review and meta-analysis to determine the effect of sperm DNA damage on in vitro fertilization and intracytoplasmic sperm injection outcome
Bibliographic record
Abstract
Sperm DNA damage is prevalent among infertile men and is known to influence natural reproduction. However, the impact of sperm DNA damage on assisted reproduction outcomes remains controversial. Here, we conducted a meta-analysis of studies on sperm DNA damage (assessed by SCSA, TUNEL, SCD, or Comet assay) and clinical pregnancy after IVF and/or ICSI treatment from MEDLINE, EMBASE, and PUBMED database searches for this analysis. We identified 41 articles (with a total of 56 studies) including 16 IVF studies, 24 ICSI studies, and 16 mixed (IVF + ICSI) studies. These studies measured DNA damage (by one of four assays: 23 SCSA, 18 TUNEL, 8 SCD, and 7 Comet) and included a total of 8068 treatment cycles (3734 IVF, 2282 ICSI, and 2052 mixed IVF + ICSI). The combined OR of 1.68 (95% CI: 1.49-1.89; P < 0.0001) indicates that sperm DNA damage affects clinical pregnancy following IVF and/or ICSI treatment. In addition, the combined OR estimates of IVF (16 estimates, OR = 1.65; 95% CI: 1.34-2.04; P < 0.0001), ICSI (24 estimates, OR = 1.31; 95% CI: 1.08-1.59; P = 0.0068), and mixed IVF + ICSI studies (16 estimates, OR = 2.37; 95% CI: 1.89-2.97; P < 0.0001) were also statistically significant. There is sufficient evidence in the existing literature suggesting that sperm DNA damage has a negative effect on clinical pregnancy following IVF and/or ICSI treatment.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.008 | 0.010 |
| 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) | 0.005 | 0.001 |
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".