Sperm DNA fragmentation testing: Summary evidence and clinical practice recommendations
Bibliographic record
Abstract
We herein summarise the evidence concerning the impact of sperm DNA fragmentation in various clinical infertility scenarios and the advances on sperm DNA fragmentation tests. The collected evidence was used to formulate 41 recommendations. Of these, 13 recommendations concern technical aspects of sperm DNA fragmentation testing, including pre-analytical information, clinical thresholds and interpretation of results. The remaining 28 recommendations relate to indications for sperm DNA fragmentation testing and clinical management. Clinical scenarios like varicocele, unexplained infertility, idiopathic infertility, recurrent pregnancy loss, intrauterine insemination, in vitro fertilisation/intracytoplasmic sperm injection, fertility counselling for men with infertility risk factors and sperm cryopreservation have been contemplated. The bulk evidence supporting the recommendations has increased in recent years, but it is still of moderate to low quality. This guideline provides clinicians with advice on best practices in sperm DNA fragmentation testing. Also, recommendations are provided on possible management strategies to overcome infertility related to sperm DNA fragmentation, based on the best available evidence. Lastly, we identified gaps in knowledge and opportunities for research and elaborated a list of recommendations to stimulate further investigation.
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".