Establishing the European Norm for the health-related quality of life domains of the computer-adaptive test EORTC CAT Core
Bibliographic record
Abstract
OBJECTIVE: The computer-adaptive test (CAT) of the European Organisation for Research and Treatment of Cancer (EORTC), the EORTC CAT Core, assesses the same 15 domains as the EORTC QLQ-C30 health-related quality of life questionnaire but with increased precision, efficiency, measurement range and flexibility. CAT parameters for estimating scores have been established based on clinical data from cancer patients. This study aimed at establishing the European Norm for each CAT domain based on general population data. METHODS: We collected representative general population data across 11 European Union (EU) countries, Russia, Turkey, Canada and the United States (n ≥ 1000/country; stratified by sex and age). We selected item subsets from each CAT domain for data collection (totalling 86 items). Differential item functioning (DIF) analyses were conducted to investigate cross-cultural measurement invariance. For each domain, means and standard deviations from the EU countries (weighted by country population, sex and age) were used to establish a T-metric with a European general population mean = 50 (standard deviation = 10). RESULTS: A total of 15,386 respondents completed the online survey (n = 11,343 from EU countries). EORTC CAT Core norm scores for all 15 countries were calculated. DIF had negligible impact on scoring. Domain-specific T-scores differed significantly across countries with small to medium effect sizes. CONCLUSION: This study establishes the official European Norm for the EORTC CAT Core. The European CAT Norm can be used globally and allows for meaningful interpretation of scores. Furthermore, CAT scores can be compared with sex- and age-adjusted norm scores at a national level within each of the 15 countries.
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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 | 0.049 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".