Clinical Observation of Acellular Dermal Matrix Allograft plus Autogenous Split-Thickness Skin Grafts in Deep Burn Wound Repair
Bibliographic record
Abstract
Objective: To explore effective ways to repair deep burn wounds.Methods: 30 cases were divided into two groups.The treatment group was treated with acellular allograft derma plus autogenous split-thickness skin graft.The control was treated simply with autogenous split-thickness skin graft.The Vancouver scat scale score was tested in the 1st,6th,12th month after the operation.Results: The difference of the Vancouver scat scale score between two groups at one month after operation has no statistical significance.The Vancouver scat scale score of the treatment group is lower than the control group in the 6th,12th,month after operation,and the difference has statistical significance.Conclusions: Acellular allograft derma plus autogenous split-thickness skin graft is an effective way for deep burn wounds repair.
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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 | 0.000 | 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".