Low level laser therapy as a modality to attenuate cytokine storm at multiple levels, enhance recovery, and reduce the use of ventilators in COVID-19
Bibliographic record
Abstract
The global pandemic COVID-19 is a contagious disease and its mortality rates ranging from 1% to 5% are likely due to acute respiratory distress syndrome (ARDS), and cytokine storm. A significant proportion of patients who require intubation succumb to the disease, despite the availability of ventilators and the best treatment practices. Researchers worldwide are in search of anti-inflammatory medicines in the hope of finding a cure for COVID-19. Low-level laser therapy (LLLT) has strong, anti-inflammatory effects confirmed by meta-analyses, and it may be therapeutic to ARDS. LLLT has been used for pain management, wound healing, and other health conditions by physicians, physiotherapists, and nurses worldwide for decades. In addition, it has been used in veterinary medicine for respiratory tract disease such as pneumonia. Laser light with low-power intensity is applied to the surface of the skin to produce local and systemic effects. Based on the clinical experience, peer-reviewed studies, and solid laboratory data in experimental animal models, LLLT attenuates cytokine storm at multiple levels and reduces the major inflammatory metabolites. LLLT is a safe, effective, low-cost modality without any side-effects that may be combined with conventional treatment of ARDS. We summarize the effects of LLLT on pulmonary inflammation and we provide a protocol for augmenting medical treatment in COVID-19 patients. LLLT combined with conventional medical therapy has the potential to prevent the progression of COVID-19, minimize the length of time needed on a ventilator, enhance the healing process, and shorten recovery time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".