Artificial dermis combined with split-thickness skin autograft in the treatment of hand thermal compression wounds: a single center case-control study
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".