Sperm DNA Fragmentation may Influence IUI Outcome but could be Treated by ICSI: Evidence from Human Sperm Bank
Bibliographic record
Abstract
Objective To determine the correlation between semen parameters, sperm DNA damage, progressive motility (PR), morphology and intrauterine insemination (IUI)/intracytoplasmic sperm injection (ICSI) outcomes. Methods All the donors providing the samples in this study were recruited by Shanghai Human Sperm Bank. For IUI, 122 donors were divided into group A (n=60) and group B (n=62). Group A had a higher pregnancy rate while group B had a lower pregnancy rate (3.86 ± 1.50% vs 0.18 ± 0.52%). For ICSI, 45 donors were divided into group C with a higher pregnancy rate (77.78 ± 17.21%, n=23), group D with a lower pregnancy rate (40.73 ± 19.19%, n=22) and group E with an average pregnancy rate in the sperm bank (48.96 ± 12.08%, n=23). Semen analysis, morphology and DNA damage were assessed on samples retained in the sperm bank. Fresh semen samples were also collected and corresponding semen analyses data was included along with the pregnancy rates. Results No significant difference was found in the population characteristics between groups A and B, while there was a significant difference in sperm DNA fragmetation index (DFI) and morphology between the two groups (P 0.05). There was no significant difference in population characteristics between groups C, D and E while the DFI of group D was significant higher than groups C and E (P 0.05). Conclusion DFI might be a good predictor for IUI outcomes. Infertile couples with a high DFI should choose ICSI treatment instead of IUI. DFI should be a routine screening marker used to screen for sperm donors.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".