Evaluación de cicatrización en zonas donantes de injerto de piel parcial con uso de Xenoinjerto en comparación con sustituto dérmico
Bibliographic record
Abstract
Objective: To evaluate healing in partial skin graft donor sites using a skin substitute compared to a xenograft in patients with different diseases requiring partial skin grafting. Materials and methods: This paper presents a report of 20 patients between 19 and 65 years from the Plastic Surgery Unit of the Hospital María Auxiliadora in Lima Metropolitan Area, Peru, between December 2017 and June 2018, where healing was evaluated in partial skin graft donor sites. An interventional, analytical, prospective and longitudinal study was conducted using a double-blind design to control possible biases. For the statistical significance analysis, nonparametric tests with a 95 % confidence interval were used. Results: Using a skin substitute, a better healing quality of donor sites of epithelialization was seen compared with xenografting. Both techniques were evaluated with the Vancouver scale, which considers five aspects (healing, vascularity, pigmentation, flexibility and height), out of which healing showed significant results (p<0.05). Estimation of the risk in the healing process according to the Cox proportional hazards model showed that H = 0.60 (95 % CI 0.46- 0.78), which indicates that the shortest healing time was found in the skin substitute group. Conclusions: Skin substitutes are an important alternative that favors the good quality of healing in donor sites. skin substitutes proved to be more effective than conventional xenografting when evaluated and compared using the Vancouver healing scale.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".