Debridement method optimisation for treatment of deep dermal burns of the forearm and hand
Bibliographic record
Abstract
Introduction Surgical debridement of marginal deep dermal burns of the forearm and hand frequently is too aggressive to residual healthy skin. Additional operation is needed - split thickness skin grafting. Donor site complications should be taken in consideration, also transplanted skin rejection and ulceration. Therefore, clinical trials should be targeted to assess effectiveness of alternative debridement methods. Materials and Methods Our team performed a randomised, controlled, parallel-group clinical trial designed to compare enzymatic, mechanical, and autolytic debridement methods for the treatment of deep dermal burns of the forearm and hand. Laser Doppler Imaging (LDI) performed on the third day post-burn, was used to predict burn wound healing time. Patients who LDI predicted burn wound healing time of no more than three weeks, were included in the study. For the first (control) group received standard treatment - dressings with 1% silver sulphadiazine cream. The second patient group was treated with hydrocolloid dressings to promote autolytic debridement. The third patient group received a combination treatment - dressings with silver sulphadiazine and mechanical debridement using special single-use monofilament polyester fibre pads. The fourth group was treated with application of enzymatic dressings. The treatment period for each patient was 3 weeks, which was followed by assessment at 6 months to evaluate post-burn scars. Results There were 82 patients with deep dermal burns of the forearm and hand included in the trial, with a minimum of 20 patients in each treatment group The fastest burn wound healing was observed in the patient group treated with hydrocolloid dressings. Furthermore, the quality of scars according to the Vancouver Scare Scale (VSS) and return of function of the injured extremity according to Disabilities of the Arm, Shoulder and Hand Outcome Measure (DASH) [...].
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".