Linguistic and psychometric validation of the MSSS-88 questionnaire for patients with multiple sclerosis and spasticity in Germany
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is an inflammatory disease where many of the patients suffer from spasticity impacting their quality-of-life. The purpose of this paper was to linguistically validate and psychometrically test the Multiple Sclerosis Spasticity Scale (MSSS-88) in German speaking MS patients. METHODS: The study had two stages: 1) forward/backward translations of the original MSSS-88 scale into German, discussions with MS-experts and cognitive debriefings with MS patients; 2) psychometric evaluation of the German version. Data collection took part in an observational multi-centre study in Germany (MOVE2). RESULTS: The German translation of the MSSS-88 scale was discussed with three MS-experts; followed by two cognitive debriefing sessions with 12 MS patients. For psychometric evaluation the MSSS-88 was filled in by 87 MS patients with a mean age of 50.2 ± 10.4 years; 26.4% of them had severe spasticity. Data quality was acceptable. Missing data for items of the MSSS-88 were low (range 0-5.75%). Psychometric testing of the MSSS-88 revealed excellent values for reliability and validity. Significant differences between groups regarding severity, grading, type and self-ratings of MS-spasticity and sleep disturbances were found. Sensitivity to change could be demonstrated for the MSSS-88 in the group of MS patients treated with cannabinoid oromucosal spray vs. non-treated patients. In the treated group significant changes with a moderate effect size were found for 'muscle spasms', 'emotional health' and 'pain/discomfort'. No significant changes could be detected in the non-treated group. CONCLUSION: Preliminary evidence from this small study supports reliability, validity, and responsiveness of the German version of the MSSS-88 for measuring the impact of spasticity in 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".