Sperm DNA: organization, protection and vulnerability: from basic science to clinical applications--a position report
Bibliographic record
Abstract
This article reports the results of the most recent in a series of EHSRE workshops designed to synthesize the current state of the field in Andrology and provide recommendations for future work (for details see Appendix). Its focus is on methods for detecting sperm DNA damage and potential application of new knowledge about sperm chromatin organization, vulnerability and repair to improve the diagnosis and treatment of clinical infertility associated with that damage. Equally important is the use and reliability of these tests to identify the extent to which environmental contaminants or pharmaceutical agents may contribute to the incidence of sperm DNA damage and male fertility problems. A working group (for workshop details, see Appendix) under the auspices of ESHRE met in May 2009 to assess the current knowledgebase and suggest future basic and clinical research directions. This document presents a synthesis of the working group's understanding of the recent literature and collective discussions on the current state of knowledge of sperm chromatin structure and function during fertilization. It highlights the biological, assay and clinical uncertainties that require further research and ends with a series of 5 key recommendations.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".