Improved sperm DNA fragmentation levels in infertile men following very short abstinence of 3–4 hours
Bibliographic record
Abstract
Background: Limited data exists on possible approaches to improve sperm DNA fragmentation index (DFI) when no identifiable cause is found. The effect of short abstinence on sperm parameters has been extensively studied, but rarely reported on the effect on DFI in infertile men. In this study, we aimed to determine whether a second ejaculate provided after very short abstinence demonstrates lower DFI rates in infertile men. Methods: This prospective cohort study was conducted at Mount Sinai Hospital, Toronto, Canada, a tertiary university affiliated hospital. All men having DFI testing in addition to the standard semen analysis were identified via a prospectively collected database. Infertile men were instructed to provide two semen samples 3-4 hours apart (the first sample was given after 2-5 days of abstinence) to test the effect on DFI levels. Data analysis was performed for the comparison of the change in sperm parameters and DFI between samples and between men with DFI above and under 30%. Results: . 1.5±0.9 mL, P<0.001), while the remaining parameters did not change. Forty out of 52 patients (76.9%) had improved DFI (average of 6.0±4.0 percentage points). Change in DFI varied with 22/52 (42.3%) and 7/52 (13.5%) of patients found to have decreases in DFI >5% and >10% in the second ejaculate, respectively. For men with DFI of 30-40%, 64% (7/11) of DFIs reduced to the under 30% range. First DFI value was the only parameter associated with DFI decrease to under 30% in multivariate models [odds ratio (OR), 0.62; 95% confidence interval (CI): 0.39-0.98; P=0.04]. Conclusions: This study identified significant improvements in DFI in infertile men providing a second sample after 3-4 hours. Controlled trials are needed to determine if reproductive outcomes are improved using a second ejaculate for infertile men with high initial sperm DFI values.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".