Psychometric Properties of the SymptoMScreen Questionnaire in a Mild Disability Population of Patients with Relapsing–Remitting Multiple Sclerosis: Quantifying the Patient’s Perspective
Bibliographic record
Abstract
Crucial elements for achieving optimal long-term outcomes in multiple sclerosis (MS) are patient confidence and effective physician-patient communication. Patient-reported instruments may provide the means to fill the gap in currently available clinician-rated measures. The SymptoMScreen (SMSS) is a brief self-assessment tool for measuring symptom severity in 12 neurologic domains commonly affected by MS. We conducted a non-interventional study to assess the dimensional structure and item characteristics of the SMSS. A total of 218 patients with relapsing-remitting MS and mild disability (median Expanded Disability Status Scale score 2.0) were studied. Symptom severity was low (SMSS score 13.5, interquartile range 4.2-27), fatigue being the domain with the highest impact. A non-parametric item response theory, i.e., Mokken analysis, found that the SMSS is a robust one-dimensional scale (overall scalability index H 0.60) with high reliability (Cronbach's alpha 0.94). The confirmatory factor analysis model confirmed the unidimensional structure (comparative fit index 1.0, root-mean-square error of approximation 0.001). Samejima's model fitted well an unconstrained model with different item difficulties. The SMSS shows appropriate psychometric characteristics and may constitute a valuable and easy-to-implement addition to measure the symptom severity in clinical practice.
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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 | 0.005 | 0.016 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".