Longitudinal study of sperm DNA fragmentation as measured by terminal uridine nick end-labelling assay
Bibliographic record
Abstract
BACKGROUND: One major limitation in the use of sperm DNA fragmentation as measured by the TdT (terminal deoxynucleotidyl transferase)-mediated dUTP nick-end labelling (TUNEL) assay is the paucity of solid data on the stability of this parameter. METHODS: The objective of our study was to evaluate variations in the degree of sperm DNA fragmentation, as measured by the TUNEL assay, over a 6 month period. Five donors provided semen samples (total 107) on the average three times per month, and 10 infertility patients provided semen samples every 4 weeks (total 58). RESULTS: The mean percentage of sperm DNA fragmentation for donors was 13.18%, the within-donor standard deviation (SD(W) = 3.79%) was small compared to between-donor (SD(B) = 17.56%). For the group of patients, the mean percentage of sperm DNA fragmentation was 22.44%, with SD(W) of 4.43% within patients and SD(B) of 29.48% between patients. No seasonal rhythm was observed during the study. The intra-class correlation coefficient for all subjects combined was 0.83. Compared to sperm concentration, individual coefficients of variation for sperm DNA fragmentation indicated less variability in four subjects, but were similar in the others. CONCLUSION: This longitudinal study shows that sperm DNA fragmentation is a parameter with good stability (repeatability) over time; it can be taken as a baseline both in healthy fertile men and in patients from infertility couples.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".