Uso de xenoinjerto comparado con sustituto dérmico sintético de nanocelulosa en pacientes con quemaduras termicas de segundo grado profundo (enero 2022 a julio 2023)
Bibliographic record
Abstract
Objective: To compare scar formation between regenerated nanocellulose dermal substitutes and xenografts in patients with deep second-degree thermal burns. Materials and methods: A comparative, interventional, analytical, prospective and longitudinal study was conducted. We present the report of 60 cases evaluated in a private clinic in Lima, Peru, between January 2022 and July 2023. Patients aged 1 to 60 years without comorbidities were evaluated for scar formation from deep second-degree thermal burns within the first 24 hours of the accident. Both dermal substitutes were used in all patients. The study was authorized with informed consent. Results: An evaluation was conducted at 90 days, showing better scar formation with the synthetic dermal substitute made of nanocellulose compared to the xenograft. The results were evaluated using the Vancouver Scar Scale (VSS) (vascularization, pigmentation, pliability and height) and showed that the synthetic dermal substitute made of nanocelullose had less redness and greater elasticity, which were the most favorable indicators. The importance of the study lies in evaluating the quality of scar formation with the use of two treatments for deep second-degree burns. Conclusions: It was evident that the synthetic dermal substitute made of nanocellulose is an important alternative that favors the quality of scar formation in burned areas. It has proved to be more efficient than xenograft when evaluated and compared across its four parameters using the VSS, an international tool for wound healing evaluation. This efficient alternative for the treatment of second-degree burns promotes a better scar formation process, providing an adequate environment for healing under improved conditions.
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 | 0.000 | 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".