Artificial dermis combined with split-thickness skin autograft in the treatment of hand thermal compression wounds: a single center case-control study
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Bibliographic record
Abstract
Objective To explore the clinical effect of artificial dermis combined with split-thickness skin autograft in treating hand thermal compression wounds. Methods Forty-two patients in our hospital from January 2016 to October 2022 with thermal compression wounds were divided into two groups. The survival rate of autologous skin grafts seven days after skin grafting, the number of operations, total hospital stay, total hospitalization cost, and bacterial culture results of secretions were recorded. The visual analog scale was used to evaluate the wound pain. The condition of skin graft rupture was recorded and the scar status of the donor site was evaluated by the Vancouver Scar Scale. Results It showed combination of artificial dermis, split-thickness skin autograft, and vacuum sealing drainage improves the treatment of hand thermal compression wounds by enhancing the survival rate of skin grafting (95.24% > 66.67%), reducing the number of operations ( P < 0.001), relieving wound pain ( P < 0.001), effectively controlling wound infection (4.76% < 9.52%), and reducing the skin graft rupture rate after surgery (4.8% < 28.6%). There was no evident scar hyperplasia in the donor ( P = 0.003) and skin graft areas ( P < 0.001), which had a good recovery of hand function ( P = 0.037); however, this treatment strategy may prolong the hospital stay ( P = 0.030) and increase the total hospitalization cost ( P = 0.030). Conclusion The composite transplantation of artificial dermis and split-thickness skin combined with the VSD significantly improves treatment and aesthetic outcomes in patients with thermal compression wounds to the hand, which is worth promoting and applying in clinical practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it