Cross-cultural adaption, translation and validation of the Toronto extremity salvage score (TESS) for patients in German-speaking countries
Bibliographic record
Abstract
OBJECTIVE: The preferred treatment for malignant bone and soft tissue tumors is limb salvage surgery; the Toronto extremity salvage score (TESS) is commonly used to measure physical functioning of the affected extremity. The aims of this study were to translate and culturally adapt the German version of the TESS, as well as to explore its convergent reliability, validity and re-test reliability. STUDY DESIGN: Patients (n = 50) 32 lower extremity (LE) and 18 upper extremity (UE) were asked to fill out the German TESS two times (t1: clinical visit, t2: regular email) and the SF-36 once. METHODS: The TESS questionnaires were translated from English into German, back translated into English, and culturally adapted. The reliability was assessed with Cronbach's alpha (α). The validity was measured with the SF-36 physical component score and TESS using the Spearman rank correlation coefficient (r). Furthermore, the test-retest reliability was calculated with the intraclass correlation coefficient (ICC). RESULTS: Internal consistency for both questionnaires was excellent (LE t1: α = 0.924, t2: α = 0.952; UE t1: α = 0.957, t2: α = 0.898). A statistically significant correlation was found between the SF-36 physical component scale and the German TESS (LE r = 0.741, UE r = 0.713). The ICC between baseline (t1) and re-test (t2) was 0.952 and 0.871 for the lower and upper extremities, respectively. CONCLUSION: Initial evidence demonstrated that the German TESS is a valid and reliable instrument for use with patients after surgical treatment of malignant bone or soft tissue 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 | 0.007 | 0.012 |
| 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) | 0.001 | 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".