Cause-Specific Treatment in Patients with High Sperm DNA Damage Resulted in Significant DNA Improvement
Bibliographic record
Abstract
Assessment of sperm DNA damage has been suggested as a negative predictor of fertility potential. Multiple pathological factors acting at both the intra-testicular and post-testicular levels may contribute to sperm DNA damage. The relative contribution of each of these factors in an individual with high DNA damage (>30%) is unclear. The management of patients with elevated DNA damage is also challenging. The purpose of our retrospective study was to evaluate the clinical course of patients with sperm DNA damage over 30% and to assess the effect of non-specific (oral antioxidant) and cause-specific treatments on the quality of their sperm DNA. Results of our retrospective study suggest that the evaluated group with high DNA damage was diagnostically heterogeneous and comprised patients with varicoceles, bacteriospermia and idiopathic infertility. A three month course of antioxidant therapy reduced sperm DNA damage in only 30/61 (49%) patients with significant improvement between the initial and post-treatment DNA Fragmentation Index (DFI) results (46.8%+/-14.1 vs. 36.7%+/-16.6, p < .001). The positive effect of antioxidants could be age-dependent, as patients older that 40 years of age showed no improvement after such treatment. The cause-specific treatments showed superior results compared to antioxidants alone. Improvement was observed in 7/9 (78%) of patients after surgical varicocele repair between the initial and post-treatment DFI results (44.7%+/-12.8 vs. 28.4%+/-9.5, p < .03). The majority of the patients 13/14 (93%) with bacteriospermia had improvement in sperm DFI results after antibiotic treatment (50.4%+/-19.1 vs. 38.6%+/-18.7, p < .001).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".