Cross-Cultural Validation of the Italian Version of the Bt-DUX: A Subjective Measure of Health-Related Quality of Life in Patients Who Underwent Surgery for Lower Extremity Malignant Bone Tumour
Bibliographic record
Abstract
The purpose of this study was to translate the English bone tumour DUX (Bt-DUX-Eng) questionnaire for lower extremity bone tumour patients, a disease-specific quality of life (QoL) instrument, into Italian and then examine the validity of the Italian version of Bt-DUX (Bt-DUX-It). The adaptation and translation process included forward translation, back-translation, and a review of the back-translation by an expert committee. The Bt-DUX-It was validated in a sample of adolescents treated for lower extremity osteosarcoma in Italy. Assessments included the Bt-DUX, the Toronto Extremity Salvage Score (TESS), and the European Organization for Research and Treatment Core Quality of Life Questionnaire of Cancer Patients (EORTC QLQ-C30). Fifty-one patients with a median age of 20 years (range: 15–25) completed the questionnaires. The mean Bt-DUX score was 70 (range: 16.30–100). The internal consistency of the overall score and that of the Bt-DUX-It was good: Cronbach’s α was 0.95. Spearman’s correlation coefficient between the Bt-DUX (total and domain scores) and EORTC QLQ C30 and TESS were overall moderate to good, reaching a p-value <0.01 in all cases. The Bt-DUX-It version is a useful tool for measuring QoL in patients with bone tumour and has similar internal consistency, construct validity, and discrimination as those of the Dutch and English versions.
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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 | 0.011 |
| 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 | 0.000 | 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".