There Is a Positive Dose-Dependent Association between Low-Dose Oral Minoxidil and Its Efficacy for Androgenetic Alopecia: Findings from a Systematic Review with Meta-Regression Analyses
Bibliographic record
Abstract
Background: Recently, low-dose oral minoxidil (LDOM) has entered the landscape of therapies for androgenetic alopecia (AGA). We determined whether using LDOM is associated with improving AGA in a dose-dependent manner; secondarily, we examined whether a dose-dependent association also exists for safety. Methods: Systematic searches were conducted in PubMed and Scopus to identify studies that would be eligible for our quantitative analyses; the logistics of our analyses was determined by the data we gathered. Results: Six studies were eligible for quantitative analyses; we conducted meta-regressions. We found that, for persons with AGA, increasing the dosage of LDOM by 1 mg/day was – after six months – significantly associated with an expected sex-adjusted increase in hair diameter (mean difference = 1.4 μm, p = 0.01), total hair density (mean difference = 47.1 hairs/cm2, p = 0.007), terminal hair density (mean difference = 9.1 hairs/cm2, p = 0.001), risk of hypertrichosis (mean difference = 17.9%, p = 0.006), and cardiovascular adverse events (mean difference = 4.8%, p = 0.004). Conclusions: Our study produced new evidence as our work is the first to show a positive dose-dependent association between the use of LDOM and change in hair diameter, hair density, risk of hypertrichosis, and cardiovascular adverse events for persons with AGA. Future randomized trials could produce causal evidence that would corroborate these dose-dependent associations.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.037 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".