Assessing the EORTC QLQ-BM22 Module Using Rasch Modeling and Confirmatory Factor Analysis across Countries: a Comprehensive Psychometric Evaluation in Patients with Bone Metastases
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Bibliographic record
Abstract
BACKGROUND: The European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Bone Metastases Module (EORTC QLQ-BM22) is a recently designed supplement to EORTC Quality of Life Questionnaire-C30 (EORTC QLQ-C30). Additional psychometric properties, especially using confirmatory factor analysis (CFA) and the Rasch model, are warranted. MATERIALS AND METHODS: A total of 573 patients with bone metastases were enrolled from eight countries with a mean±SD age of 55.8±13.7 years. Slightly more than two thirds of them were female (n=383; 66.8%). CFA was used to examine the BM22 framework; Rasch models were applied to understand misfit items and differential item functioning (DIF). RESULTS: The fit indices were satisfactory in CFA (comparative fit index=0.972, Tucker-Lewis index=0.964, root mean square error of approximation=0.076, and standardized root mean square residual=0.045). All items fit well in the Rasch models (mean square values were between 0.5 and 1.5), and only one item (number 17) displayed DIF across gender. However, there were six DIF items across Canada and Taiwan, ten across Canada and Iran, and six across Taiwan and Iran. CONCLUSIONS: The BM22 has satisfactory psychometric properties, and could assess the QoL of patients with bone metastases specifically focusing on their symptoms. Clinicians may want to use it to capture the underlying QoL for patients with bone metastases. However, the score of item 17 should be interpreted with caution when comparing male and female patients. In addition, researchers should note that variation in DIF items may occur when conducting an international study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it