Home-use Photobiomodulation Device Treatment Outcomes for COVID-19
Bibliographic record
Abstract
ABSTRACT BACKGROUND There is need for non-pharmaceutical treatments for COVID-19. A home-use photobiomodulation (PBM) device was tested as Treatment in a randomized clinical trial. METHODS 294 patients were randomized with equal allocation to Treatment or Standard of Care (Control). 199 qualified for efficacy analyses. The Treatment group self-treated for 20 minutes twice daily, for the first 5 days, and subsequently once daily for 30 days. A validated respiratory questionnaire was used, and patients were monitored remotely. The primary endpoint was the time-to-recovery (3 consecutive days of no sickness) for general sickness. The Kaplan-Meier method and the Cox Proportional Hazards model were primary methods of analyses. RESULTS Treatment patients with collective 0-12 days of symptoms, at moderate-to-severe level on Day 1 of Treatment, did not recover significantly faster than Control. However, for patients with 0-7 days of symptoms there was a significant mean difference of 3 days: Treatment, 18 days (95% CI, 13-20) vs. Control, 21 days (95% CI, 15-28), P=0.050. The Treatment:Control hazard ratio at 1.495 (95% CI, 0.996-2.243), P=0.054 exceeded the pre-trial target of 1.44. Treated patients exceeding 7 days symptoms duration were more tired and had lower energy. None of the patients in the Treatment group suffered death or hospitalization while the Control group had 1 death and 3 severe adverse events requiring hospitalization. CONCLUSIONS Patients with up to 7 days of symptoms at moderate-to-severe levels on first day of Treatment can expect faster recovery for general sickness and several respiratory symptoms. (Funded by Vielight Inc.; ClinicalTrials.gov number, NCT04418505 .)
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".