Minimal clinically important difference in myasthenia gravis: Outcomes from a randomized trial
Bibliographic record
Abstract
INTRODUCTION: The minimal clinically important difference (MCID) is the smallest outcome change that has clinical significance. Its use has not been established in the study of myasthenia gravis (MG). METHODS: Patients from a published intravenous immunoglobulin (IVIg) vs. placebo study were studied. One anchor-based and 3 distribution-based techniques were used to identify quantitative myasthenia gravis score (QMGS), repetitive nerve stimulation (RNS), and single-fiber electromyography (SFEMG) MCID cut-offs. Patients with a change-score exceeding MCID cut-offs were compared. RESULTS: MCID cut-offs were below a QMGS change of 3.0. Anchor-based and 1 × SEM cut-offs showed 58.3% vs. 30.7% responders (P = 0.017), ½ SD 54.2% vs. 19.2% responders (P = 0.018), and effect size 0.519 vs. 0.164 (P = 0.011) in IVIg vs. placebo. Anchor-based (P = 0.73) and effect-size (P = 0.41) MCID cut-offs did not show a difference between IVIg and placebo. MCID methods did not produce meaningful RNS cut-offs. CONCLUSIONS: QMGS MCID values provide clinically relevant information and are recommended in MG trials. MCID analysis shows that improvement in MG patients treated with IVIg reflects clinically meaningful changes.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".