Relative Effects of Minoxidil 5%, Platelet-Rich Plasma, and Microneedling in Pattern Hair Loss: A Systematic Review and Network Meta-Analysis
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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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.036 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".