Translation and validation of non-English versions of the Ankylosing Spondylitis Quality of Life (ASQOL) questionnaire
Bibliographic record
Abstract
BACKGROUND: The Ankylosing Spondylitis Quality of Life (ASQOL) questionnaire is a unidimensional, disease-specific measure developed in the UK and the Netherlands. This study describes its adaptation into other languages. METHODS: The UK English ASQOL was translated into US English; Canadian French and English; French; German; Italian; Spanish; and Swedish (dual-panel methods). Cognitive debriefing interviews were conducted with AS patients. Psychometric/scaling properties were assessed using data from two Phase III studies of adalimumab. Baseline and Week-2 data were used to assess test-retest reliability. Validity was determined by correlation of ASQOL with SF-36 and BASFI and by discriminative ability of ASQOL based on disease severity. Item response theory (Rasch model) was used to test ASQOL's scaling properties. RESULTS: Cognitive debriefing showed the new ASQOL versions to be clear, relevant and comprehensive. Sample sizes varied, but were sufficient for: psychometric/scaling assessment for US English and Canadian English; psychometric but not scaling analyses for German; and preliminary evidence of these properties for the remaining languages. Test-retest reliability and Cronbach's alpha coefficients were high: US English (0.85, 0.85), Canadian English (0.87, 0.86), and German (0.77, 0.79). Correlations of ASQOL with SF-36 and BASFI for US English, Canadian English, and German measures were moderate, but ASQOL discriminated between patients based on perceived disease severities (p < 0.01). Results were comparable for the other languages. US English and Canadian English exhibited fit to the Rasch model (non-significant p-values: 0.54, 0.68), confirming unidimensionality. CONCLUSION: The ASQOL was successfully translated into all eight languages. Psychometric properties were excellent for US English, Canadian English, and German, and extremely promising for the other languages.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".