Adjunct low level laser therapy (LLLT) in a morbidly obese patient with severe COVID-19 pneumonia: A case report
Bibliographic record
Abstract
Introduction COVID-19 poses a higher risk of complications in obese patients due to low respiratory system compliance, increased inflammatory cytokines, and an activated immune system secondary to excess adiposity. Low level laser therapy (LLLT) has significant anti-inflammatory effects and reduces inflammatory cytokines. It is noninvasive and approved for pain management and musculoskeletal injuries. Data from human and experimental animal models of respiratory tract disease suggests that LLLT reduces inflammation and promotes lung healing. Case and outcomes A morbidly obese 32-year-old Asian female with severe COVID-19 received four consecutive once-daily LLLT sessions via a laser scanner. Pulsed 808 nm and 905 nm laser beams were delivered over the posterior chest for 28 min. The patient was evaluated before and after LLLT by radiological assessment of lung edema (RALE) on chest X-ray, oxygen requirements and saturation, pneumonia severity indices (SMART-COP and Brescia-COVID), blood inflammatory markers (interleukin-6, ferritin, and C-Reactive protein (CRP)). Prior to treatment, oxygen saturation (SpO~2~) via pulse oximetry was 88%--93% on 5--6 L oxygen. Following LLLT, SpO~2~ increased to 97%--99% on 1--3 L oxygen. Reductions in RALE score from 8 to 3, Brescia-COVID from 4 to 0, and SMART-COP from 5 to 0 were observed. Interleukin-6 decreased from 45.89 to 11.7 pg/mL, ferritin from 359 to 175 ng/mL, and CRP from 3.04 to 1.43 mg/dL. Post-treatment, the patient noted appreciable improvement in respiratory symptoms. Conclusion Following LLLT our patient showed improvement over a few days in respiratory indices, radiological findings, inflammatory markers, and patient outcomes. This report suggests that adjunct LLLT can be safely combined with conventional treatment in patients with severe COVID-19 and morbid obesity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".