A retrospective study of comparison of collagen dressing versus conventional dressing for skin graft donor site
Bibliographic record
Abstract
Background: Split-skin grafting is commonly employed for covering skin defects in case of ulcers, deep burns and following trauma. It involves harvesting of the epidermis and upper 1/3rd of dermis resulting in a wound called donor site wound (DSW). These wounds pose a kind of burden to patients during the process and after the process of wound healing. These wounds tend to cause pain, are at risk of getting infected, pruritis and cosmetic inconvenience. DSW has been managed with closed or open dressings. Out of many methods, we aim to compare the efficacy of collagen dressing with that of conventional dressing in this study.Methods: A retrospective study including 30 subjects were stratified into 2 groups; group A-collagen dressing and group B- conventional dressing. Patients aged between 18 to 60 years undergoing split thickness skin grafting were included. Patients who are immunocompromised, diabetic, with underlying skin disease and infected wounds were excluded. The outcome was compared in terms of pain, pruritis and scar assessment using Vancouver scar scale.Results: In the present study there was significant difference in median pain score, pruritus and median Vancouver scar score in collagen group compared to conventional group at all the intervals. Also, the incidence of surgical site infection was lower in the collagen dressing group.Conclusions: Collagen dressing is superior compared to conventional dressing in terms of lower pain score, pruritus score and Vancouver scar score.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".