Effects of Low-Level Laser on Wound Healing and Quality of Life in Foot Ulcers with Diabetic Neuropathy
Bibliographic record
Abstract
BACKGROUND: Diabetic foot ulcer was characterized by the infection, ulceration, or destruction of the deep tissues associated with neurological anomalies and to a greater degree of peripheral vascular disease in the lower limbs. Low-level laser therapy has a stimulating effect on cells, whereas high-energy radiation has an inhibiting effect. So, the application of laser to stimulate wound healing in cases of non- healing ulcers has been recommended. So, the purpose of the study was to know about the effects of low- level laser therapy on wound healing and quality of life in foot ulcers with diabetic neuropathy. METHODOLOGY: A total of 30 patients fulfilled the inclusion criteria were assigned to two Groups by using a simple random sampling technique. The subjects are divided into 15 for each group. Group A received treatment with low-level laser therapy for 4 weeks, whereas Group B received conventional treatment. RESULT: After a period of therapeutic intervention using the low-level laser therapy, it was noticed that there was an improvement in the wound reduction with a significant process of tissue repair. Where as in the control group there was no reduction of wound size is seen. CONCLUSION: Group A shows more effectiveness in reducing wound size, considering to the low-level laser therapy seems to be an efficient method, viable, painless, and of low cost in tissue repair of ulcers in diabetic foot. The analgesic effect is significant for the reestablishment of these patients, preventing possible amputations and improving quality of life. Keywords: LOW- LEVEL LASER THERAPY, DIABETIC FOOT, DIABETIC FOOT ULCER
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".