Bibliographic record
Abstract
OBJECTIVE: To present a case-based discussion on the workup of male factor infertility and review currently available treatments. SOURCES OF INFORMATION: This discussion is based on the current Canadian Urological Association and American Urological Association guidelines, with reference to landmark papers as appropriate from 2010 onward. All articles were retrieved through PubMed. MAIN MESSAGE: Approximately 15% of Canadian couples experience infertility, making it a commonly encountered condition in the primary care setting. Among couples suffering from infertility, male factors can be identified as the sole cause in 30% of cases and as a contributing issue in 20% of cases. Although many of the treatments described aim to improve a couple's chances of naturally conceiving a child via intercourse, many patients ultimately require medical or surgical intervention to achieve pregnancy. This can be a long, protracted course for patients, with important roles for primary care providers and fertility specialists alike. CONCLUSION: Male fertility assessment and treatment has historically been left in the hands of fertility specialists, creating a bottleneck for patients to receive fertility care. However, with increased understanding of the underlying causes of male factor infertility, the workup and initial management can occur in the primary care setting, helping to streamline care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".