A Critical Assessment of the Evidence for Low-Level Laser Therapy in the Treatment of Hair Loss
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: 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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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