Are Tests of Sperm DNA Damage Clinically Useful? Pros and Cons
Bibliographic record
Abstract
The advent of assisted reproductive technologies, particularly intracytoplasmic sperm injection (ICSI), has revolutionized the treatment of male-factor infertility. However, there are many unanswered questions regarding the safety of these techniques. These safety concerns are relevant because 1) these technologies often bypass the barriers to natural selection; 2) infertile men, particularly those with severe male-factor infertility, possess substantially more sperm DNA damage than do fertile men; and 3) experimentally, sperm DNA damage has been shown to adversely affect the developing embryo. This review discusses the etiology of sperm DNA damage, describes the individual tests of sperm DNA damage, and explores the relationship between sperm DNA damage and pregnancy outcomes. Based on a systematic review of the literature, sperm DNA damage is associated with lower natural, intrauterine insemination (IUI), and in vitro fertilization (IVF) pregnancy rates, but not with ICSI pregnancy rates. The literature also suggests that that sperm DNA damage is associated with an increased risk of pregnancy loss in those couples undergoing IVF or ICSI. Nonetheless, the true clinical utility of sperm DNA damage tests remains to be established, because the available studies are small and few in number and the study characteristics are heterogeneous. Although current data suggest that impaired sperm DNA integrity may have the greatest effect on IUI pregnancy rates and pregnancy loss by IVF and ICSI, further prospective studies are needed before testing should become a routine part of patient management.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".