Cytogenetic risks in chromosomally normal infertile men
Bibliographic record
Abstract
PURPOSE OF REVIEW: Infertility is a growing problem that affects a surprisingly high number of couples (15%) of which the causes often remain 'unexplained'. However, more and more genetic causes underlying male infertility are emerging. RECENT FINDINGS: Research has begun to shed light on the causes of previously unexplained male infertility with clear links now established with infertility and meiotic defects in pairing, synapsis and recombination as well as increased levels of sperm aneuploidy. However, many have questioned whether this increase in sperm aneuploidy is observed in conceptuses or live birth; research suggests that this increase in aneuploidy is in fact paralleled in intracytoplasmic sperm injection (ICSI) conceptions. SUMMARY: Further research is warranted investigating the relationship between sperm aneuploidy and risk to ICSI conceptuses. Several infertility phenotypes have clearly been identified having a higher risk of sperm aneuploidy and may benefit from sperm aneuploidy screening prior to ICSI. Such screening would ultimately assist couples in deciding on the relative risk of undertaking ICSI and enable them to make informed decisions on whether to proceed with ICSI or to combine it with further screening such as preimplantation genetic diagnosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".