In the Eye of the Beholder—Skin Rejuvenation Using a Light-Emitting Diode Photomodulation Device
Bibliographic record
Abstract
BACKGROUND: A light-emitting diode (LED) photomodulation system can produce pulses of amber light expected to induce structural skin changes and reverse the effects of photoaging. OBJECTIVE: To reproduce the encouraging results already published. METHODS AND MATERIALS: Facial skin was exposed to pulses of 588+/-10-nm-wavelength light from a photomodulation device for 40 seconds once a week for 8 weeks. Photographs, clinical assessment, and a subjective questionnaire were taken at baseline, at the last follow-up, and 1 month after that. Thirty-six patients' pre- and post-treatment photos were arbitrarily scrambled, and 30 independent blinded observers were asked to pick the post-treatment photo. Two time-point comparisons were evaluated. RESULTS: For every facial characteristic studied and for both time-point comparisons, patients reported highly statistically significant improvements. In extremely sharp contrast, neither the physician's assessment nor the independent observers' evaluation indicated any improvement. CONCLUSION: Although subjective findings are comparable between studies, we were unable to reproduce the objective results of efficacy previously reported. Patients genuinely believed that several of their facial features had improved, even though there was no detectable objective change. Our data therefore suggest that the LED photomodulation treatment from the device tested is a placebo.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".