Moist exposed burn therapy in recovery of patients with immature, red hypertrophic scars successfully treated with a pulsed dye laser in combination with a fractional CO<sub>2</sub> laser
Bibliographic record
Abstract
BACKGROUND: in treatment of hypertrophic scars is well documented. The present study investigates the efficacy of moist exposed burn ointment (MEBO)/moist exposed burn therapy (MEBT) in postlaser wound management. METHODS: Sixty-one patients with immature, red hypertrophic scars were enrolled in this clinical trial. Patients were randomly divided into two groups: (a) the MEBO treatment group (n = 30) and (b) the control group (n = 31) treated with chlortetracycline hydrochloride ointment. Demographic data such as age, gender, and cause of scars were recorded. A visual analogue score (VAS) was collected to measure pain at 1, 6, 24, 72 hours, and 7 days post-treatment. The Vancouver Scar Scale (VSS) was used to determine the response of the scars before and 3 months after the treatment. The wound healing time and pigmentation scores were also recorded. RESULTS: No significant differences were found in age, gender, and etiology of the scars in the two groups. The VAS scores in MEBO group were significantly lower than the control group within the first 3 days after treatment. The wound healing time of the MEBO group was significantly shorter than the control group. For both groups, VSS scores were significantly decreased and the scar markedly improved. However, the VSS scores were significantly lower in the MEBO group compared with the control group 3 months after treatment and pigmentation formation was dramatically lower in MEBO group compared with the control. CONCLUSION: MEBT/MEBO treatment reduced the post-treatment pain, shortened the wound healing duration, promoted the overall scar condition, and reduced the incidence of pigmentation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".