Aneuploidy in human sperm: a review of the frequency and distribution of aneuploidy, effects of donor age and lifestyle factors
Bibliographic record
Abstract
Application of fluorescence in situ hybridization (FISH) analysis has opened the way for comprehensive studies on numerical chromosome abnormalities in human sperm. During the last decade, more than five million sperm from approximately 500 normal men were analyzed by a number of laboratories from around the world by this approach. Except for chromosome 19 which has been analyzed in only one study, all other chromosomes have been examined by two or more studies with considerable differences in disomy frequency for an individual chromosome among studies. The mean disomy frequency is 0.15% for each of the autosomes and 0.26% for the sex chromosomes. Most chromosomes analyzed have an equal distribution of disomy with the exception of chromosomes 14, 21, 22 and the sex chromosomes, which display significantly higher disomy frequencies. Slight but significant increases in disomy frequency with advancing paternal age were observed for some chromosomes, in particular for the sex chromosomes. Some lifestyle factors such as smoking, alcohol drinking and caffeine consumption have been investigated and no consistent association between disomy frequency and any type of lifestyle factors has been established. The question of whether different geographic and ethnic groups of men have inherent differences in frequency of disomic sperm has been investigated by two studies with conflicting results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".