Advances in the treatment of male androgenetic alopecia: current options and emerging therapies
Bibliographic record
Abstract
INTRODUCTION: Androgenetic alopecia (AGA), or pattern hair loss, is the most common form of hair loss worldwide. It is primarily caused by genetic and hormonal factors, particularly the action of dihydrotestosterone (DHT) on hair follicles. EVIDENCE ACQUISITION: A comprehensive literature search on PubMed and Google Scholar was conducted until October 31, 2024, using keywords related to male AGA and its treatments. Relevant reviews, meta-analyses, clinical trials, and case studies in English were selected and analyzed to enhance the quality of the research. This review is based solely on existing studies and does not include new human or animal research conducted by the authors. EVIDENCE SYNTHESIS: This review thoroughly examined and analyzed 149 articles to provide a detailed presentation of the evidence. CONCLUSIONS: This article examines both established and emerging therapies for AGA. We recommend initiating treatment with topical minoxidil and oral finasteride, as these are extensively researched and FDA-approved options for male AGA. For patients who do not respond well or cannot tolerate these treatments, clinicians may explore alternative therapies and approaches.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".