Translation and Cross‐Cultural Adaptation of the Toronto Extremity Salvage Score (TESS) for Latin American Spanish–Speaking Patients With Limb Sarcoma
Bibliographic record
Abstract
Background and Objectives: This study aims to translate and culturally adapt the Toronto Extremity Salvage Score (TESS) for Latin American Spanish–speaking patients, enhancing the tool’s accessibility for evaluating postsurgical functional outcomes in sarcoma patients across Latin America. Methods: The TESS questionnaires for lower extremity (LE) and upper extremity (UE) were translated and adapted following international guidelines. The process included forward and backward translation, expert committee review, and pretesting with cognitive interviewing. Patients treated for bone or soft tissue tumors in LE or UE were recruited to complete the adapted questionnaires. Test–retest reliability was evaluated by having participants complete the questionnaire again 2 weeks after the initial assessment. Results: A total of 89 participants completed the questionnaires. The study found high internal consistency, with Cronbach’s alpha values reaching 0.9437 for LE and 0.9402 for UE. An agreement rate of 98.4% for the global score of TESS‐LE (95% confidence interval [CI]: 0.909–1.059) and 93.9% for TESS‐UE (95% CI: 0.882–0.995) was observed, demonstrating strong test–retest reliability. Conclusions: The Latin American Spanish version of TESS for both lower and upper extremities is a reliable and culturally appropriate tool for assessing physical function in limb sarcoma patients. Further validation across diverse Latin American populations is encouraged to strengthen its broad applicability.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".