Evaluation of minced skin grafts in the treatment of post burn leukoderma.
Bibliographic record
Abstract
BackgroundPost burn leukoderma had been characterized by chalk white depigmented areas of variable sizes and shapes. Re-pigmentation of the hypopigmented lesion is still a big challenge, current treatment modalities for post burn leukoderma include non-surgical techniques and many surgical interventionsMethodsTwenty Patients (18 female & 2 males) with post burn leukoderma were included. Patients’ age ranging from 10 to 50 years. The minimum leukoderma surface area was 0.5 % whereas the maximum was 3%. Patients were assessmed one year post operatively using Vancouver Scar Scale and Patient Observer Scar Assessment Scale (POSAS).ResultsVancouver scar scale results were; Good pigmentation were obtained in 75% of patients, Hyperpigmentation in 20% of patients and Partial pigmentation was in 5% of patients. For POSAS The overall patient opinion scale was 1 (which denote best skin colour) in 80% of patients, score 2 in 10% of patients, score 3 in 5% of patients, score 4 in 5% of patients.ConclusionMinced skin graft can be used safely for the treatment of post burn leukoderma. It is simple reliable technique that can be easily integrated in our daily practice, no need for special instruments or laboratory preparations, gives a satisfactory result for patients with minimal donor morbidity.
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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) | 0.000 | 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".