Meta-analysis of photobiomodulation for the treatment of androgenetic alopecia
Bibliographic record
Abstract
Background Androgenetic alopecia (AGA) is a condition that affects most people at some point in their life, yet few treatments are available. Use of photobiomodulation is ideal due to the safety profile and lack of serious adverse effects. Therefore, the efficacy of photobiomodulation for AGA therapy was investigated.Methods A meta-analysis was used to elucidate treatment efficacy. Additionally, a sub-analysis was performed to determine if the type of device used or if use of lasers versus light emitting diodes (LEDs) significantly impacted results.Results Using hair density (hairs/cm2) as a measure of efficacy, the standardized mean difference (SMD) was 1.02 (95% CI: 0.68, 1.36) in favor of treatment over control (15 studies, pooled N = 795, p < .00001). Subgroup analysis comparing comb-style devices versus helmet/hat-style devices did not reveal a significant difference (p = .08). A second subgroup analysis suggested that laser treatment was significantly more effective (p = .009) than a combination of laser/LED treatment although the combination treatment was still significantly better than control treatment.Discussion Meta-analysis results suggest that photobiomodulation could be used to effectively treat AGA. Specific device recommendations should be based on use of lasers versus LEDs and not the style (comb/hat/helmet) of the device.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".