Comparison of Donor-Site Healing under Xeroform and Jelonet Dressings: Unexpected Findings
Bibliographic record
Abstract
Split-thickness skin grafts remain central to the strategy of burn wound treatment. The dressing used to cover the donor wound site has a significant effect on healing parameters. The purpose of this study was to compare split-thickness skin graft donor site reepithelialization under Xeroform and Jelonet dressings. A dermatome was used to cut two consecutive strips of skin from 25 paired donor sites on the thigh, calf, or back of 19 participants. Standardization of the harvest method was achieved by using the same surgeon to harvest the compared skin graft strips, with attention to consistency of dermatome skin-thickness setting, downward pressure, and angle of dermatome approach. A strip of Xeroform or Jelonet was applied to one of each pair of wounds. Epidermal and dermal thickness was measured from biopsy specimens cut at the midpoint of each split-thickness graft strip. The day of final dressing separation was declared the day of complete donor reepithelialization (healing). The mean healing time for Xeroform and Jelonet was 10.4 +/- 2.6 days (n = 25) and 10.6 +/- 2.8 days (n = 25) (p = 0.76) at sites cut to a mean depth of 0.23 +/- 0.08 mm and 0.23 +/- 0.09 mm (p = 0.89), respectively. There was no correlation between graft thickness and healing time for sites dressed with Xeroform (r = 0.17) or Jelonet (r = 0.02). Donors sites reharvested 10 to 21 days after a prior harvest healed an average of 3.1 days earlier than virgin sites (8.4 +/- 1.6 versus 11.5 +/- 2.6 days, p < 0.001), although reharvested grafts were on average 0.05 mm thicker (p = 0.10). The mean thickness of reepithelialized donor-site epidermis (0.13 +/- 0.04 mm, n = 30) was found to be twice the thickness of virgin epidermis from the same sites (0.06 +/- 0.02 mm, n = 38, p < 0.001). Thirty-six grafts harvested with dermatomes set to cut 8/1000 inch (0.20 mm) deep ranged from 0.12 to 0.42 mm thick, with only eight of these grafts measuring within +/-10 percent of the desired thickness setting. Before donor dressing separation, Xeroform and Jelonet dressings were judged to be more comfortable by nine patients and one patient, respectively, whereas no difference was detected by six patients. The authors now use Xeroform as the preferred donor dressing.
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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 | 0.001 |
| 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) | 0.001 | 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".