Validation of the Multiple Sclerosis International Quality of Life questionnaire
Bibliographic record
Abstract
This study aims to validate the Multiple Sclerosis (MS) International Quality of Life (MusiQoL) questionnaire, a multi-dimensional, self-administered questionnaire, available in 14 languages, as a disease-specific quality of life scale that can be applied internationally. A total of 1992 patients with different types and severities of MS from 15 countries were recruited. At baseline and day 21 +/- 7, each patient completed the MusiQoL, a symptom checklist and the short-form (SF)-36 QoL questionnaire. Neurologists also collected socio-demographic, MS history and outcome data. The database was randomly divided into two subgroups and analysed according to different patient characteristics. For each model, psychometric properties were tested and the number of items was reduced by various statistical methods. Construct validity, internal consistency, reproducibility and external consistency were also tested. Nine dimensions, explaining 71% of the total variance, were isolated. Internal consistency and reproducibility were satisfactory for all the dimensions. External validity testing revealed that dimension scores correlated significantly with all SF-36 scores, but showed discriminant validity by gender, socio-economic and health status. Significant correlations were found between activity in daily life scores and clinical indices. These results demonstrate the validity and reliability of the MusiQoL as an international scale to evaluate QoL in patients with MS.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".