Genome and Epigenome Disorders and Male Infertility: Feedback from 15 Years of Clinical and Research Experience
Bibliographic record
Abstract
Infertility affects around 20% of couples of reproductive age; however, in some societies, as many as one-third of couples are unable to conceive. Different factors contribute to the decline of male fertility, such us environmental and professional exposure to endocrine disruptors, oxidative stress, and life habits with the risk of de novo epigenetics dysregulation. Since the fantastic development of new "omes and omics" technologies, the contribution of inherited or de novo genomes and epigenome disorders to male infertility have been further elucidated. Many other techniques have become available to andrology laboratories for the investigation of genome and epigenome integrity and the maturation and the competency of spermatozoa. All these new methods of assessment are highlighting the importance of genetics and epigenetics investigation for assisted reproduction pathology and for supporting professionals in counselling patients and proposing different management strategies for male infertility. This aims to improve clinical outcomes while minimizing the risk of genetics or health problems at birth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".