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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".