Acticoat Versus Allevyn as a Split-Thickness Skin Graft Donor-Site Dressing
Bibliographic record
Abstract
The study comprises 27 operated patients with similar burns. Fifteen donor sites treated with Acticoat (Smith & Nephew) and 12 donor sites treated with Allevyn (Smith & Nephew) have been analyzed with respect to epithelization time, antibacterial effect, ease of dressing change, pain, and pharmacologic and cost-effective characteristics. All donor sites after the reepithelization were evaluated using the Vancouver Scar Scale for the assessment of scars at the fourth, eighth, and 12th weeks. The obtained results demonstrate statistically significant faster epithelization (P = 0.012 on the eighth day and P = 0.0081 on the 10th day) and better comfort for the patient with the Acticoat dressing (P < 0.05). With regard to bacterial growth (P > 0.05) there is no statistically significant difference in the application of Acticoat and Allevyn. The Vancouver Scar Scale assessment shows no statistically significant difference (P > 0.05) in the application of both Acticoat and Allevyn. There is no considerable difference in the cost of treatment between both dressings. The results obtained determine both dressings as suitable for application on donor sites. If there is a possibility of choice, the Acticoat dressing is preferable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".