Low-Level Laser and Light-Emitting Diode Therapy for Pain Control in Hyperglycemic and Normoglycemic Patients Who Underwent Coronary Bypass Surgery with Internal Mammary Artery Grafts: A Randomized, Double-Blind Study with Follow-Up
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the efficacy of low-level laser therapy (LLLT) and light-emitting diodes (LEDs) for reducing pain in hyperglycemic and normoglycemic patients who underwent coronary artery bypass surgery with internal mammary artery grafts. METHODS: This study was conducted on 120 volunteers who underwent elective coronary artery bypass graft (CABG) surgery. The volunteers were randomly allocated to four different groups of equal size (n = 30): control, placebo, LLLT [λ = 640 nm and spatial average energy fluence (SAEF) = 1.06 J/cm(2)], and LED (λ = 660 ± 20 nm and SAEF = 0.24 J/cm(2)). Participants were also divided into hyperglycemic and normoglycemic subgroups, according to their fasting blood glucose test result before surgery. The outcome assessed was pain during coughing by a visual analog scale (VAS) and the McGill Pain Questionnaire. RESULTS: The patients were followed for 1 month after the surgery. The LLLT and LED groups showed a greater decrease in pain, with similar results, as indicated by both the VAS and the McGill questionnaire (p ≤ 0.05), on the 6th and 8th postoperative day compared with the placebo and control groups. The outcomes were also similar between hyperglycemic and normoglycemic patients. One month after the surgery, almost no individual reported pain during coughing. CONCLUSIONS: LLLT and LED had similar analgesic effects in hyperglycemic and normoglycemic patients, better than placebo and control groups.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".