Differential item functioning of the Arm function in Multiple Sclerosis Questionnaire (AMSQ) by language, a study in six countries
Bibliographic record
Abstract
Background: The Arm function in Multiple Sclerosis Questionnaire (AMSQ) has been developed as a self-reported measure of arm and hand functioning for patients with multiple sclerosis (MS). The AMSQ was originally developed in Dutch and to date translated into five languages (i.e. English, German, Spanish, French, and Italian). Objective: The aim of this study was to evaluate differential item functioning (DIF) of the AMSQ in these languages. Methods: We performed DIF analyses, using “language” as the polytomous group variable. To detect DIF, logistic regression and item response theory principles were applied. Multiple logistic regression models were evaluated. We used a pseudo R 2 value of 0.02 or more as the DIF threshold. Results: A total of 1733 male and female patients with all subtypes of MS were included. The DIF analysis for the whole dataset showed no uniform or non-uniform DIF on any of the 31 items. All R 2 values were below 0.02. Conclusion: The AMSQ is validated in six languages. All items have the same meaning to MS patients in Dutch, English, German, Spanish, French, and Italian. This validation study enables use of the AMSQ in international studies, for monitoring treatment response and disease progression.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".