Split-thickness skin graft donor sites: a comparative study of two absorbent dressings
Bibliographic record
Abstract
OBJECTIVE: To identify the optimal dressing for split-thickness skin graft (SSG) donor sites. METHOD: This prospective randomised controlled trial compared two dressings - a new absorbent form of a polyurethane film dressing (Tegaderm Absorbent, 3M) and our standard alginate dressing (Kaltostat, ConvaTec) - on SSG donor sites in 40 patients. Primary outcome measures were: reduced time to full healing; reduced postoperative pain; reduced leakage rates from the dressing. Secondary outcome measures related to acceptability of the dressings to the patient. RESULTS: On removal of the dressings at the first assessment, 79% of the Tegaderm Absorbent donor sites had healed completely, compared with 16% of the Kaltostat ones (p<0.001).A significantly greater median area had healed with Tegaderm Absorbent (100%), when compared with Kaltostat (89%) (p<0.001). Mean time to complete healing was also significantly faster for Tegaderm Absorbent than Kaltostat (14 versus 21 days) (p<0.001). Significantly fewer subjects experienced postoperative pain with Tegaderm Absorbent on both day 1 (21% versus 67%, p=0.006, NNT=3) and day 2 (17% versus 75%, p<0.001, NNT=2). Leakage rates reduced by 48% with Tegaderm Absorbent, with no leakage in the smaller donor sites. Tegaderm Absorbent was significantly easier to apply than Kaltostat (89% versus 27% found it'very easy') as was ease of removal (84% versus 11% found it'very easy') (p<0.0001). Patients found Tegaderm Absorbent dressings significantly more convenient to manage and bathe with. At one month post-surgery, Vancouver scar scores showed thatTegaderm Absorbent donor sites were less red, flatter, softer and less itchy. CONCLUSION: Tegaderm Absorbent provides a significant improvement in terms of donor-site pain, healing and ease of management.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".