Sperm DNA damage: importance in the era of assisted reproduction
Bibliographic record
Abstract
PURPOSE OF REVIEW: With the proliferation of assisted reproductive technologies, there is an increasing awareness of the importance of sperm DNA damage. With the recent advent of in-vitro fertilization/intracytoplasmic sperm injection, we are bypassing the normal natural selection barriers and potentially introducing sperm with damaged DNA. To date, these consequences are still largely unknown. RECENT FINDINGS: Infertile men possess substantially more DNA-damaged sperm than fertile men, and this DNA damage may adversely affect reproductive outcomes. There may be a threshold level of DNA damage beyond which embryo development and subsequent pregnancy are impaired. Protamine deficiency and the effect of reactive oxygen species have been implicated in the etiology of sperm DNA damage. Assays of DNA damage are being used clinically to quantify objectively the degree of DNA damage in the sperm of infertile men in the hope of identifying more accurate ways of gauging fertility potential. SUMMARY: There now exists clinical evidence to show that sperm DNA damage is detrimental to reproductive outcomes. Tests for DNA damage may provide better prognostic information and may allow for better decision-making than standard semen parameters when evaluating the infertile couple. The etiology of the DNA damage and the full extent of the damage on reproductive outcomes need further study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".