Cross-cultural adaptation and validation of the Japanese version of the Toronto Extremity Salvage Score (TESS) for patients with malignant musculoskeletal tumors in the upper extremities
Bibliographic record
Abstract
Background The Toronto Extremity Salvage Score (TESS) is a widely used disease-specific patient-completed questionnaire for the assessment of physical function in patients with musculoskeletal tumors ; however, there had not been the validated Japanese version of the TESS. The aim of this study was to validate the Japanese version of the TESS in patients with musculoskeletal tumors in the upper extremity. Methods After developing a Japanese version of the TESS, the questionnaire was administered to 53 patients to examine its reliability and validity in comparison with the Musculoskeletal Tumor Society (MSTS) scoring system and Short Form-36 (SF-36). Results Test–retest reliability with intraclass correlation coefficient (0.93) and internal consistency with Cronbach's alpha (0.90) were excellent. Factor analysis showed that the construct structure consisted of 3-item clusters, and the Akaike Information Criterion network also demonstrated that the items could be divided into 3 domains according to their content. The TESS strongly correlated with the MSTS rating scale (r = 0.750; P < 0.001) and the SF-36 physical functioning scale (r = 0.684; P < 0.001). However, as expected, the TESS had low correlations with the SF-36 mental health and role-emotional subscales and the MSTS scoring system manual dexterity domain. Conclusions Our study suggests that the TESS is a reliable and valid instrument to measure patient-reported physical functioning in patients with upper extremity sarcoma.
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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 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".