Efficacy of non-surgical treatments for androgenetic alopecia in men and women: a systematic review with network meta-analyses, and an assessment of evidence quality
Bibliographic record
Abstract
Various treatments exist for androgenetic alopecia (AGA); we determined the relative efficacies of non-surgical AGA monotherapies separately for men and women. Randomized controlled trials (RCTs) were systematically searched in PubMed, EMBASE, Scopus and clinicaltrials.gov. Separate networks were used for men and women; for each network, a Bayesian network meta-analysis (NMA) of mean change in hair count from baseline (in units of hairs per square centimeter) was performed using a random effects model. The networks for male and female AGA included 30 and 10 RCTs, respectively. We identified the following treatments for male AGA in decreasing rank of efficacy: platelet-rich plasma (PRP), low-level laser therapy (LLLT), 0.5 mg dutasteride, 1 mg finasteride, 5% minoxidil, 2% minoxidil, and bimatoprost. For female AGA the following were identified in decreasing rank of efficacy: LLLT, 5% minoxidil, and 2% minoxidil. The evidence quality of the highest ranked therapies, for male and female AGA, was judged to be low. While newer treatments like LLLT may be more efficacious than more traditional therapies like 5% minoxidil, the efficacy of the more recent treatment modalities needs to be further validated by future RCTs.
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.012 | 0.010 |
| 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) | 0.004 | 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".