Antisperm antibodies are not associated with pregnancy rates after IVF and ICSI: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Several studies have examined the relationship between direct antisperm antibody (ASA) levels in semen and pregnancy rate after advanced assisted reproductive technologies (ARTs) but the results have been inconsistent. The aim of our study was to further evaluate the relationship between ASA and pregnancy after IVF or ICSI by systematic review and meta-analysis. METHODS: We conducted a systematic Medline search of all relevant full papers on direct semen ASA and pregnancy after IVF or ICSI. Three investigators independently reviewed the papers, followed by group discussion to choose the included papers. Meta-analysis was performed to get an odds ratio (OR) for the effect of ASA on pregnancy using IVF or ICSI. RESULTS: The study identified and analyzed 16 valid studies (10 IVF and 6 ICSI). The study characteristics (including the ASA cutoff values) were heterogeneous. Our meta-analysis revealed that the combined OR for failure to achieve a pregnancy using IVF or ICSI in the presence of positive semen ASA was 1.22 (95% CI: 0.84, 1.77) and 1.00 (95% CI: 0.72, 1.38), respectively. The overall (IVF + ICSI) combined OR was 1.08 (95% CI: 0.85, 1.38). CONCLUSION: This systematic review and meta-analysis indicate that semen antisperm antibodies are not related to pregnancy rates after IVF or ICSI, suggesting that both forms of ART remain viable options for infertile couples with semen ASA. However, additional, well-designed prospective studies using appropriate ASA cutoff levels are needed to further address this issue.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| 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".