The Quantitative Myasthenia Gravis Score
Bibliographic record
Abstract
OBJECTIVE: To determine whether the quantitative myasthenia gravis score (QMGS) accurately represents disease severity in patients with myasthenia gravis (MG). METHODS: One hundred thirty-five patients with MG from 2 previous randomized studies were included. QMGS correlation with the Myasthenia Gravis Foundation of America (MGFA) score, quality of life scale, acetylcholine receptor antibodies (AChRAbs), and electrophysiological parameters was studied. RESULTS: The QMGS showed a good correlation with the MGFA scale (r² = 0.54, P < 0.0001), jitter (rs = 0.40, P < 0.0001), and 15-item quality of life scale (rs = 0.41, P = 0.007) and was less well correlated with the 60-item myasthenia gravis-specific quality of life survey and other electrophysiological markers. No correlation was demonstrated with AchRAb titers, but AchRAb-positive patients had higher QMGS (14.2 ± 4.5) than AchRAb-negative patients (12.0 ± 3.7, P = 0.008). CONCLUSIONS: These results demonstrate that the QMGS is a valid marker for disease severity as shown by the MGFA scale, quality of life scale, and jitter, supporting the use of the QMGS as a primary outcome measure in clinical trials of MG.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".