Translation and cross-cultural adaptation of the Toronto extremity salvage score system into Arabic and its validity
Bibliographic record
Abstract
BACKGROUND: The treatment of musculoskeletal (MSK) tumors involving the extremities has evolved over the past decade with the introduction of prosthesis and new chemotherapy regimen. The Toronto Extremity Salvage Score system (TESS) is a patient-filled questionnaire that measures the functional status of patients with MSK tumors who underwent limb-salvaging procedure. The purpose of this study is to translate TESS into Arabic (TESS-AR) and to examine its reliability and validity. METHODS: Our study is a multi-center in Riyadh, Saudi Arabia. Arabic-speaking adults diagnosed with MSK tumors involving the extremities were included. TESS-AR was created following clear, user-friendly guidelines for translation. Moreover, reliability and validity were measured using the test-retest method and construct validity, respectively. RESULTS: 108 participants completed the TESS-AR, 56% had lower limb tumors. The participants reported that the TESS-AR was clear and all questions and answers were understood. The test-retest reliability showed excellent reliability, with an interclass correlation coefficient of 0.965 for both the lower and upper extremity TESS-AR. Cronbach's alpha of lower extremity TESS-AR was 0.972, whereas that of upper extremity TESS-AR was 0.969, indicating strong internal consistency. The construct validity between TESS-AR and SF-36 showed a strong and moderated correlation between most of the components, with a Pearson correlation coefficient >0.40. Similar results were found between TESS-AR and EORTC QLQ C30. CONCLUSION: The TESS-AR is a comprehensible, valid, and reliable score for assessing functional outcomes in patients with extremity tumors. We believe that TESS-AR can be used by clinicians, researchers, and patients alike.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".