An Evidence-Based Perspective to the Medical Treatment of Male Infertility: A Short Review
Bibliographic record
Abstract
INTRODUCTION: Evidence-based medicine (EBM) is the integration of best research evidence with clinical expertise and patient preferences and values. AIM: This narrative review aims to assist the physicians to make informed decisions based on the best available evidence in the area of male infertility and the patients' own preferences and values. METHODS: In this review we present the current state of knowledge and uncertainties about the medical management of male infertility. We describe the best available evidence from systematic reviews, randomized controlled studies and observational studies where appropriate. RESULTS: Data from the literature suggest that gonadotropin treatment of male infertility can lead to a significant increase in pregnancy rates, however larger studies are needed to confirm such findings. Studies including combinations of antiestrogens, antioxidants and androgens are promising but need confirmation with further research. CONCLUSIONS: Most current combination therapies consist of orphan medications without industry support. Andrology research centers and other dedicated departments and units need to conduct randomized controlled trials of sufficient duration, sample number and robust design for groups most likely to benefit from antiestrogens, L-carnitine, antioxidants, and combination therapy. The ease of administration, low cost and mild side effects of antiestrogens justify their utility despite insufficient evidence of effect as monotherapies. Randomized controlled trials assessing other forms of medical therapy and combination therapy are available but are still in the preliminary stages.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".