Psychometric Properties of the Quantitative Myasthenia Gravis Score and the Myasthenia Gravis Composite Scale
Bibliographic record
Abstract
BACKGROUND: The Quantitative Myasthenia Gravis Score and the Myasthenia Gravis Composite are two commonly used outcome measures in Myasthenia Gravis. So far, their measurement properties have not been compared, so we aimed to study their psychometric properties using the Rasch model. METHODS: 251 patients with stable myasthenia gravis were assessed with both scales, and 211 patients returned for a second assessment. We studied fit to the Rasch model at the first visit, and compared item fit, thresholds, differential item functioning, local dependence, person separation index, and tests for unidimensionality. We also assessed test-retest reliability and estimated the Minimal Detectable Change. RESULTS: Neither scale fit the Rasch model (X2p < 0.05). The Myasthenia Gravis Composite had lower discrimination properties than the Quantitative Myasthenia Gravis Scale (Person Separation Index: 0.14 and 0.7). There was local dependence in both scales, as well as differential item functioning for ocular and generalized disease. Disordered thresholds were found in 6(60%) items of the Myasthenia Gravis Composite and in 4(31%) of the Quantitative Myasthenia Gravis Score. Both tools had adequate test-retest reliability (ICCs >0.8). The minimally detectable change was 4.9 points for the Myasthenia Gravis Composite and 4.3 points for the Quantitative Myasthenia Gravis Score. CONCLUSIONS: Neither scale fulfilled Rasch model expectations. The Quantitative Myasthenia Gravis Score has higher discrimination than the Myasthenia Gravis Composite. Both tools have items with disordered thresholds, differential item functioning and local dependency. There was evidence of multidimensionality in the QMGS. The minimal detectable change values are higher than previous studies on the minimal significant change. These findings might inform future modifications of these tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".