A Critical Assessment of the Evidence for Low-Level Laser Therapy in the Treatment of Hair Loss
Bibliographic record
Abstract
BACKGROUND: Low-level laser therapy (LLLT) is currently in use to stimulate hair growth and is quickly gaining in popularity due to the ease of use and absence of side effects. In 2015 alone, the number of LLLT devices with the Food and Drug Administration clearance has doubled. OBJECTIVE: To consolidate evidence and establish which data are still required for the widespread acceptance of LLLT for hair loss therapy. METHODS AND MATERIALS: A thorough search of the PubMed database was conducted to obtain studies investigating LLLT for androgenetic alopecia in men and women. RESULTS: Nine trials were identified for comb and helmet/cap devices, five of which were randomized controlled trials. Data comparison across LLLT trials and with traditional hair loss therapy (minoxidil, finasteride) was not straight forward because there was a lack of visual evidence, sample sizes were low, and there were large variations in study duration and efficacy measurements. CONCLUSION: There are a number of unanswered questions about the optimum treatment regimen, including maintenance treatment and the long-term consequences of LLLT use. Moving forward, protocols should be standardized across trials. Moreover, it is recommended that future trials include visual evidence and trial duration be expanded to 12 months.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".