The relative effect of monotherapy with 5‐alpha reductase inhibitors and minoxidil for female pattern hair loss: A network meta‐analysis study
Bibliographic record
Abstract
BACKGROUND: Minoxidil and the 5-alpha reductase inhibitors (5-ARIs), specifically, dutasteride and finasteride, are usually used to treat pattern hair loss (PHL), but evidence on the relative effectiveness of these drugs is far less for women than men. AIMS: We performed an age-adjusted network meta-analysis (NMA) to determine the comparative efficacy of monotherapy with the three agents-in any dosage and administrative route-on PHL in adult women. METHODS: The peer-reviewed literature was systematically reviewed to obtain data for our NMA. The outcome measure for our NMA was "change in total hair density." We referred to "regimen" as an "agent and its dosage;" our Bayesian NMA estimated regimens' surface under the cumulative ranking curve (SUCRA) values and pairwise relative effects. RESULTS: Our NMA used data from 13 trials-across which the following 10 regimens were identified (in decreasing order of SUCRA): 5 mg/day finasteride for 24 weeks (SUCRA = 95.7%), 5% topical minoxidil solution twice daily for 24 weeks (SUCRA = 89.5%), 1 mg/day minoxidil for 24 weeks (SUCRA = 78.1%), 5% topical minoxidil foam 1 half capful/day for 24 weeks (SUCRA = 66.5%), 3% topical minoxidil solution 1 mL twice daily for 24 weeks (SUCRA = 45.1%), 2% topical minoxidil solution 1 mL twice daily for 24 weeks (SUCRA = 44.6%), 5% topical minoxidil solution 1 mL/day for 24 weeks (SUCRA = 41.7%), 0.25 mg/day minoxidil for 24 weeks (SUCRA = 35.5%), 1.25 mg/day finasteride for 24 weeks (SUCRA = 24.8%) and 1 mg/day finasteride for 24 weeks (SUCRA = 4.3%). CONCLUSION: Our findings can improve clinical guidelines and help dermatologists manage female PHL more optimally with the available options.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.037 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".