The impact of monotherapies for male androgenetic alopecia: A network meta‐analysis study
Bibliographic record
Abstract
Abstract Background The evidence base pertaining to the efficacy of monotherapies for androgenetic alopecia (AGA), the most common form of hair loss, is ever expanding—and this warrants a formal comparison therapies' effect on a frequent basis. Aims The objective of the current study was to determine the comparative effect of relevant monotherapies for male AGA. Patients/Methods Our aim was achieved by conducting Bayesian network meta‐analysis (NMA), under a random effects model, for two outcomes: 6‐month change in (1) total and (2) terminal hair density in adult (i.e., aged 18 years and above) men with AGA; these analyses were preceded by a systematic search of the peer‐reviewed literature for suitable data. Interventions' surface under the cumulative ranking curve (SUCRA) and pairwise relative effects (quantified as mean differences) were estimated through the NMAs. Results We determined the comparative effect of 20 active comparators and a control (i.e., placebo/vehicle). "Dutasteride 0.5 mg once daily for 24 weeks" was ranked the most effective in terms of 6‐month change in (1) total hair density (SUCRA = 87%) and terminal hair density (SUCRA = 98%). Our results showed that interventions' effectiveness can be dose dependent. Conclusions Our updated analyses of the up‐to‐date evidence regarding monotherapies for male AGA showed that the oral form of 5‐alpha reductase inhibitors are more effective than oral minoxidil and other newer agents like Botox, microneedling, and photobiomodulation. Our findings can better inform clinical decision making and design of future research studies.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.028 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".