The Effect of Combination of Red, Infrared and Blue Wavelengths of Low-Level Laser on Reduction of Abdominal Girth: A Before-After Case Series
Bibliographic record
Abstract
Introduction: The objective of this study was to assess the efficacy of a combination of three wavelengths (including red, infra-red, and blue) of low-level laser (LLL) as a non-invasive therapeutic method to reduce abdominal girth. To achieve biochemical activity on adipocytes, a red laser was used in our selective laser combination. Near-infrared laser was used to increase depth of penetration. Nitrosyl complexes of hemoglobin (NO-Hb) are sensitive to blue light, thereby leading to increase in release of biologically active nitric oxide (NO), which can affect tissue perfusion. Thus, a blue LED was added to the laser combination.Methods: [W1] Eighteen females participated in the study. Twelve sessions of laser therapy were performed, two sessions per week for each subject. Continuous wave diode lasers, including red (630nm), infra-red (808nm), and a blue LED (450nm) were applied and were all designed by the Canadian Optic and Laser Center.Results: Statistical analyses revealed that upper abdomen size significantly decreased from pre- (91.86 ± 11.16) to post- (87.41 ± 10.52) low level laser therapy (LLLT) (p<0.001). Middle abdomen size showed significant reduction from pre- (97.02 ± 8.82) to post- (91.97 ± 8.49) LLLT (p<0.001). Lower abdomen size significantly decreased from pre- (100.36 ± 9.45) to post- (95.80 ± 8.52) LLLT (p<0.001).Conclusion: Based on this case series pilot investigation, the combination of 3 different wavelengths of LLL was effective for abdominal girth reduction in 100% of our subjects (p<0.001), without any side effects. Future studies will assess the long-term benefits of this laser combination for reduction of subcutaneous fat deposits.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".