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
BACKGROUND AND OBJECTIVE: Various treatments exist for androgenetic alopecia (AGA); we determined the relative efficacies of non-surgical AGA monotherapies separately for men and women. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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 | 0.031 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".