An Open-Label Study of Low-Level Laser Therapy Followed by Autologous Fibroblast Transplantation for Healing Grade 3 Burn Wounds in Diabetic Patients
Bibliographic record
Abstract
Introduction: Low-level laser therapy (LLLT) has been used as an effective therapeutic modality since the mid-1960s. Although there have been several clinical studies using LLLT in wound healing, especially diabetic, pressure and venous ulcers, there are few reports of using this technique in burn ulcers. Autologous fibroblast transplantation is a novel treatment for patients with burns or venous ulcers. In this study for the first time, we used LLLT along with autologous fibroblast skin transplantation to treat grade 3 burn ulcers in diabetic patients. This case series describes the successful management of grade 3 burn ulcers in 10 diabetic patients using autologous fibroblast transplantation along with LLLT. Methods: After the approval of the Tehran University Ethics Committee (IR.TUMS.REC.1394.1683) and the Iran Registry of Clinical Trials (IRCT2016050226069N3), 10 diabetic patients with 10 grade 3 burn ulcers, who were a candidate for skin graft surgery, entered the study. Donor skin was biopsied using a 3 mm punch. Fibroblasts were extracted and cultured in vitro in the GMP Technique laboratory. The patients were treated using LLLT in 3-4 weeks during the time that fibroblast cultures became ready to use. Laser irradiation was done using red light, 650 nm, 150 mW, 1 J/cm2 for the bed of the ulcer and infra-red light 808 nm, 200 mW, 6 J/cm2 for the margins every other day for 10 sessions. Results: The mean wound size before treatment was 16.28 cm2 . All patients’ burn wounds healed completely after 10-12 weeks. Conclusion: We conclude that this method can be used as an effective method for treating large wounds, especially in complicated patients including the diabetics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".