Cross-cultural adaptation and validation of the Chinese version of Toronto Extremity Salvage Score for patients with extremity sarcoma
Bibliographic record
Abstract
OBJECTIVE: As a widely used instrument for patients with extremity sarcoma, the Toronto Extremity Salvage Score (TESS) has never been cross-culturally adapted for Chinese population. The objective of our study was to investigate the comprehensibility, reliability and validity of the Chinese version of TESS for use in patients with extremity sarcoma. METHODS: A consensus version of the Chinese TESS was developed under the review of a committee according to international guidelines. 64 patients were recruited to complete the Chinese TESS, the Musculoskeletal Tumor Society (MSTS) Rating Scale, and the Quality of Life Questionnaire Core 30 (QLQ-C30). Reliability was assessed using the intra-class correlation coefficient (ICC) and Cronbach's α. Validity was assessed with Pearson's correlation between the similar domains of the two questionnaires. RESULTS: The ICCs for the test-retest reliability was 0.932 for the upper extremity questionnaire and 0.893 for lower extremity questionnaire, respectively. The Cronbach's α was 0.953 for the lower extremity questionnaire and 0.921 for the upper extremity questionnaire, respectively. Convergent validity of the TESS based on Pearson correlation coefficients indicated significantly moderate to high correlations between the TESS and the MSTS as well as the QLQ-C30, with r ranging from 0.535 to 0.782. CONCLUSIONS: The Chinese TESS is a comprehensible, reliable, and valid instrument that can be utilized for future cross-cultural international studies of 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 | 0.006 | 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 | 0.001 | 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".