Biophotonic Therapy with Fluorescent Light Energy Decreases Facial Erythema, Improves Signs and Symptoms of Rosacea, and Increases Patient Satisfaction: A Postmarket Study.
Bibliographic record
Abstract
BACKGROUND: Rosacea is a difficult-to-manage chronic inflammatory skin condition reported to have a negative psychosocial impact on patients. Novel approaches are sought to target the many signs and symptoms of the condition while also improving the quality of life of patients. OBJECTIVE: biophotonic platform (KLOX Technologies Inc., Laval, Canada), which creates fluorescent light energy (FLE), to induce a novel form of photobiomodulation for treating rosacea. We also assessed patient satisfaction with their facial appearance and concerns about perceptions of others before and after treatment. METHODS: Nine patients were treated once a week for four weeks with FLE. Patients and the treating clinician completed questionnaires throughout and after the treatment to grade the rosacea signs and symptoms and capture patients' perceptions of the treatment and their condition. RESULTS: FLE significantly reduced the inflammatory erythematous reaction of the face, improved flushing and erythema associated with rosacea, and had a positive impact on patients' self-perception and emotional wellbeing. CONCLUSION: Our results support FLE as an effective, noninvasive treatment modality for rosacea.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".