Bibliographic record
Abstract
There has been an increase in the use of sperm DNA and chromatin integrity tests in the evaluation of the infertile man with the hypothesis that these tests may better diagnose infertility and predict reproductive outcomes. This review discusses the etiology of sperm DNA damage, briefly describing the tests of sperm DNA damage, and evaluates the relationship between sperm DNA damage and reproductive outcomes. A systematic review of the literature allows us to conclude that sperm DNA damage is associated with lower natural, intra-uterine insemination (IUI), and in vitro fertilization (IVF) pregnancy rates. Studies to date have not shown a clear association between sperm DNA and chromatin defects and pregnancy outcomes after intra-cytoplasmic sperm injection (ICSI). However, we cannot exclude the possibility that very high levels of DNA damage will impact on ICSI outcomes. In couples undergoing IVF or ICSI, there is evidence to show that sperm DNA damage is associated with an increased risk of pregnancy loss. A limitation of this systematic review and meta-analysis is that it does not address the heterogeneity of the individual study characteristics. Although the clinical utility of tests of sperm DNA damage remains to be firmly established, the data suggest that there is clinical value in testing couples prior to assisted reproductive technologies (ARTs IUI, IVF, and ICSI) and in those couples with recurrent miscarriages. Additional, well-designed prospective studies are needed before testing becomes a routine part of patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".