Tipos de escalas para avaliação e classificação das lesões na pele: uma revisão integrativa
Bibliographic record
Abstract
Objetivo: Buscar e analisar as evidências cientificas, nas literaturas nacionais e internacionais sobre os tipos de escalas de tratamento das lesões em pele. Métodos: Trata-se de uma revisão integrativa da literatura nas seguintes bases de dados: BVS, PubMed, WEB OF SCIENCE. Resultados: Na totalidade os artigos citaram 19 ferramentas, as mais citadas foram: Pressure Ulcer Scale for Healing (PUSH) (24,32%), Bates-Jensen Wound Assessment Toll (BWAT)/Pressure Sore Status Tool (PSST) (16,22%), Patient and Observer Scar Assessment Scale (POSAS) (10,81%), Modified Vancouver Scar Scale (mVSS) e DISGN-R (5,41%), suas aplicações foram na avalição da ferida e prática do instrumento (52,94%), a criação de novos instrumentos (17,65%) e validação (29,41%). Considerações Finais: Autores relataram positivamente a utilização dessas tecnologias, sendo um recurso importante para monitorar o progresso de cicatrização, possibilitando desde a confirmação diagnóstica ao manejo preventivo e terapêutico com base científica, auxiliando de forma segura e com qualidade o cuidado prestado aos pacientes. A inovação tecnológica de novas ferramentas é de grande importância para o aumento da eficiência do serviço de saúde.
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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 | 0.092 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".