Genetic counselling for infertile men of known and unknown etiology
Bibliographic record
Abstract
Of the couples trying to conceive (had frequent, unprotected sexual intercourse for a year or longer) 15% will experience infertility with the annual incidence of infertility estimated at 1.2 couples per 1,000 total in the general population. Male factors contribute to over 50% of the cases with 7% of the male population experiencing infertility. Not being able to conceive a child is emotionally traumatic and frustrating and can affect the person's self esteem and the couple's relationship. Major progress has been achieved in identifying the etiology of male infertility and especially the genetic causes. However, in about 40% of the male infertility cases, the etiology remains unknown and both the diagnosis and/or treatment are a challenge. Genetic testing to determine the underlying genetic cause of infertility is not 100% and genes involved are still being discovered. Consequently, negative genetic test results do not rule out a genetic cause. Thus, genetic counselling should include information regarding the genetic etiology, if known, and the treatment options available. Furthermore, when the infertile couple/male is seeking assisted reproductive technology (ART) using intracytoplasmic sperm injection (ICSI), genetic counselling should include information regarding the risk of transmitting the genetic disorder, causing the male infertility, to the offspring. Therefore, the provision of genetic counselling is an integral component in the investigation and treatment of male infertility. This article will discuss the genetic counselling approach in cases with male infertility.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 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".