Comparison and Evaluation of the Low-Level Laser and the Red and Blue LED Effects on Wound Healing in Rabbit
Bibliographic record
Abstract
Introduction: Wound healing is a dynamic, interactive process to achieve the restoration of skin integrity and proper function after damage. Applying a low-level laser (LLL) and light emitting diodes (henceforth LEDs) is introduced in previous studies to accelerate the process of wound healing. The aim of this study is to compare the effect of the LLL and LEDs on wound healing in rabbits. Methods: Full thickness same size square excision wounds were created on the dorsum of the rabbits. Twenty rabbits were randomly divided into four groups, according to the treatment received. Group 1: the AlGalInP (aluminium gallium indium phosphide) laser (4 J/cm²); group 2: the red LED (30 J/cm²); group 3: the blue LED (60 J/cm²) and group 4, as the control group, was not irradiated. After 30 days, the wounds were evaluated both morphologically and histopathologically. Statistical significance was defined as a P value of less than 0.05. Results: All interfering methods including the LLL and LEDs had better outcome compared with the control group of both sizes and histopathologic features. The red laser group showed better results compared to the control group and either the LED groups. Comparing LEDs, the red LED performed better than the blue LED. Conclusion: This study confirmed the significant effects of the LLL and LEDs on wound healing. Comparing the LLL and LED, the LED may be a better choice, especially for bedridden or debilitated patients. The LED may also more cost effective in wound healing in comparison with the LLL.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".