Relative Effects of Minoxidil 5%, Platelet-Rich Plasma, and Microneedling in Pattern Hair Loss: A Systematic Review and Network Meta-Analysis
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
Background: Combination treatments may improve the utility of approved agents for the treatment of pattern hair loss (PHL); however, head-to-head comparisons are lacking. Objective: The aim of the study was to compare the efficacy of 5% minoxidil, platelet-rich plasma (PRP), and microneedling across adults with PHL insofar as change in total hair density at 24 weeks. Methods: We conducted a literature search in July 2022. Through our Bayesian network meta-analysis, we estimated treatments’ surface under the cumulative ranking distribution (SUCRA) values and relative effects – in terms of mean difference (MD). Results: Data from 27 trials, totaling 1,110 patients, were extracted. Interventions were ranked based on the probability of inducing hair density improvements: 5% minoxidil plus microneedling (SUCRA = 95.8%), 5% minoxidil plus PRP (SUCRA = 64.7%), 5% minoxidil (SUCRA = 53.9%), PRP (SUCRA = 34.9%), microneedling (SUCRA = 27.8%), and PRP with microneedling (SUCRA = 22.9%). The efficacy of 5% minoxidil plus microneedling in improving total hair density was significantly greater (p < 0.05) than 5% minoxidil monotherapy (MD = 13 hairs/cm2), PRP monotherapy (MD = 16 hairs/cm2), and microneedling monotherapy (MD = 17 hairs/cm2). Conclusion: Five percent minoxidil plus microneedling is an effective treatment option for improving hair density at 6 months in adult PHL patients.
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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) | 0.013 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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 it