Cross-Cultural Adaptation, Translation, and Validation of the Toronto Extremity Salvage Score for Extremity Bone and Soft Tissue Tumor Patients in Netherlands
Bibliographic record
Abstract
Purpose . The aim of this study was to translate and culturally adapt the Toronto Extremity Salvage Score (TESS) to Dutch and to validate the translated version. Methods . The TESS lower and upper extremity versions (LE and UE) were translated to Dutch according to international guidelines. The translated version was validated in 98 patients with surgically treated bone or soft tissue tumors of the LE or UE. To assess test-retest reliability, participants were asked to fill in a second questionnaire after one week. Construct validity was determined by computing Spearman rank correlations with the Short Form- (SF-) 36. Results . The internal consistency (0.957 and 0.938 for LE and UE, resp.) and test-retest reliability (intraclass correlation coefficients 0.963 and 0.969 for LE and UE, resp.) were good for both questionnaires. The Dutch LE and UE TESS versions correlated most strongly with the SF-36 physical function dimension ( r = 0.737 for LE, 0.726 for UE) and the physical component summary score ( r = 0.811 and 0.797 for LE and UE). Interpretation . The Dutch TESS questionnaire for lower and upper extremities is a consistent, reliable, and valid instrument to measure patient-reported physical function in surgically treated patients with a soft tissue or bone tumor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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) | 0.001 | 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".