Relative Efficacy of Conventional Monotherapies and Select Nonconventional, Over‐the‐Counter Products for Male Androgenetic Alopecia: A Network Meta‐Analysis Study
Bibliographic record
Abstract
ABSTRACT Background Society values a full head of hair. Therefore, androgenetic hair loss (AGA), though medically benign, can cause significant emotional distress. There is strong demand for alternative (nonconventional, over‐the‐counter) AGA treatments. It is important to have evidence on the efficacy of these treatments for AGA—especially in comparison with treatments that are approved by the United States Food and Drug Administration (FDA), such as oral finasteride and topical minoxidil. Aims Following a systematic review, we conducted a network meta‐analysis (NMA) to determine the relative efficacy of conventional monotherapies and selected alternative (nonconventional, over‐the‐counter) products for male AGA. Methods We conducted a Bayesian NMA under a fixed effect model with uniform priors; the NMA estimated relative effects—as per mean difference (MD), along with the 95% credible interval (CI)—and surface under the cumulative ranking curve (SUCRA) values. We also assessed study‐level evidence quality. Eligible studies were identified through systematic searches (without date restrictions) in PubMed and Scopus on April 30, 2025. The main outcome measure was change in total hair density at 24 weeks from baseline (in hairs/cm 2 ). Results and Conclusion We found 24 eligible trials—where the relative efficacy of eight conventional monotherapies and seven alternative (nonconventional, over‐the‐counter) products was determined. The current NMA study confirms the efficacy of conventional monotherapies such as oral dutasteride, topical/oral minoxidil, and oral/topical finasteride for male AGA. We have provided guidance regarding the relative efficacy of some alternative (nonconventional, over‐the‐counter) agents (e.g., melatonin (topical) and rosemary oil (topical)) compared to conventional treatments.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.040 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".