Emerging and traditional 5-α reductase inhibitors and androgen receptor antagonists for male androgenetic alopecia
Bibliographic record
Abstract
INTRODUCTION: Androgenetic alopecia (AGA) is the most prevalent cause of male hair loss, often requiring medical and/or surgical intervention. The US FDA has approved topical minoxidil and oral finasteride for male AGA treatment. However, some AGA patients fail to respond satisfactorily to these FDA-approved treatments and/or may experience side effects, based on their individual profiles. To mitigate the shortcomings of these treatments, researchers are now exploring alternative treatments such as newer 5-α reductase inhibitors (5-ARIs) and androgen receptor antagonists (ARAs). AREAS COVERED: This article reviews the safety and effectiveness of well-known 5-α reductase inhibitors (5-ARIs) like finasteride and dutasteride, as well as the newer 5-ARIs, emerging androgen receptor antagonists (ARAs), and natural products such as saw palmetto and pumpkin seed oil in the treatment of male AGA. EXPERT OPINION: Although several newer 5-ARIs, ARAs, and natural products have exhibited promise in clinical trials, additional research is essential to confirm their safety and efficacy in treating male AGA. Until additional evidence is available for these agents, the preferred treatment choices for male AGA are the FDA-approved treatments, topical minoxidil, and oral finasteride.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".