Translation, adaptation and validation of the Toronto Symptom Assessment System for Wounds (TSAS-W) to Portuguese
Bibliographic record
Abstract
Effective symptom management and patient comfort require a systematic assessment to better control symptoms. The Toronto Symptom Assessment System for Wounds (TSAS-W) is a tool designed to evaluate the complexity and specificity of wounds, focusing not on healing but on the effective control of symptoms. This study aims to translate, adapt and validate the TSAS-W for the Portuguese population and to analyze its feasibility. This is a methodological study involving the cross-cultural adaptation of a quantitative, cross-sectional, observational, and descriptive tool. This resulted in an instrument formed of 10 items. Data collection was conducted in two oncology hospitals and within a Continuing Care Network, between October 2018 and May 2019 encompassing a sample of 90 Individuals with 94 chronic wounds. The tool demonstrated good internal consistency, with a Cronbach's alpha coefficient of 0.827 in the first evaluation and 0.867 in the second evaluation. Observers confirmed the feasibility of the tool in a clinical setting. This validation came to fill the lack of recording instruments for non-healing wounds, and emphasizes patient comfort.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".