망상화한 Matriderm과 부분층 피부이식술을 이용한 화상 창상의 치료
Bibliographic record
Abstract
Purpose: Skin graft is the gold standard surgical treatment in\nburn wound management. But it has functional and aesthetic\nlimitations, such as burn scar contracture, low quality of the\ngrafted skin, unnatural looking skin, loss of skin elasticity, especially\nin extensive deep burn wound which has tendon or\nbone exposed. The authors used MatridermⓇ, a dermal analogue,\nwith split-thickness skin graft simultaneously in burn\nwound and evaluate the effectiveness of MatridermⓇ for\ntreatment of burn wounds, in comparison with the skin graft\nonly.\nMethods: 40 burn patients with skin graft were included in\nthis study. Patients were selected with their consent for inclusion\nin an experimental group and a control group.\nPatients in the experimental group received a meshed\nMatridermⓇ appliance and a split-thickness skin graft, while\nthose in the control group received only a split-thickness skin\ngraft. Time to complete epithelization, rates of skin graft taken\nareas, Vancouver scar scale assessment, skin elasticity\nwas evaluated.\nResults: A better scores of Vancouver scar scale assessment\n(3 points) were observed in the experimental group with\nthe control group (6 points) with statistical significance (P\n＜0.05). A higher elasticity ratio of the affected side to the\nnon-affected side was observed in the experimental group,\ncompared with the control group (P＜0.05), and a similar\ntime to complete epithelization and rates of skin graft taken\nareas were observed in the experimental group when compared\nwith the control group.\nConclusion: Meshed MatridermⓇ enables effective healing and improves functional and aesthetic results in split thickness\nskin graft treatment of burn wounds.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".