Cultural adaptation, translation and validation of a functional outcome questionnaire (TESS) to Portuguese with application to patients with lower extremity osteosarcoma
Bibliographic record
Abstract
BACKGROUND: Evaluation of physical functioning is an important tool for planning rehabilitation. Instruments need to be culturally adapted for use in non-English speaking countries. The aim of this study was to culturally adapt, including translation and preliminary validation, the Toronto extremity salvage score (TESS) for Brazil, in a sample of adolescents and young adults treated for lower extremity osteosarcoma. PROCEDURE: The process included two independent forward translations of TESS questionnaire, consensus between translators on a forward translation, back-translation by two independent translators, and a review of the back-translations. Internal consistency of the TESS and known groups validity were also evaluated. RESULTS: Internal consistency for the 30 item TESS was high (coefficient alpha = 0.87). TESS score ranges from 0 to 100. Forty-eight patients completed the questionnaire and scores ranged from 56 to 100 (mean score: 89.6). Patients receiving no pain medications scored higher on the TESS than those who were receiving pain medication (P = 0.014), and patients using walking aids had slightly higher but not statistically different scores. Those who were treated with amputation had higher scores than those who were treated with limb salvage procedures (P = 0.003). CONCLUSION: Preliminary evidence suggests that Brazilian-Portuguese translation is acceptable, understandable, reliable, and valid for evaluating the function in adolescents and young adults with osteosarcoma in lower extremity in Brazil.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".