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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<b><i>Background:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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, <i>p</i> = 0.01), total hair density (mean difference = 47.1 hairs/cm<sup>2</sup>, <i>p</i> = 0.007), terminal hair density (mean difference = 9.1 hairs/cm<sup>2</sup>, <i>p</i> = 0.001), risk of hypertrichosis (mean difference = 17.9%, <i>p</i> = 0.006), and cardiovascular adverse events (mean difference = 4.8%, <i>p</i> = 0.004). <b><i>Conclusions:</i></b> 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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it