Validation of the Italian version of the Myasthenia Gravis Impairment Index (MGII)
Bibliographic record
Abstract
OBJECTIVE: To validate the Italian version of the Myasthenia Gravis Impairment Index (MGII). INTRODUCTION: MGII is a recent promising measure developed for MG patient evaluation. It includes a clinical severity evaluation and a patient-reported questionnaire. It has been developed in English and has demonstrated feasibility, reliability, and construct validity. Recently, its Dutch translation has been validated. METHODS: MGII was translated to Italian with a multi-step forward process. We assessed correlations with the following scores: Istituto Nazionale Carlo Besta score for Myasthenia Gravis (INCB-MG), the MG Activities of Daily Living (MG-ADL), the Myasthenia Gravis Composite (MGC), the Quality of Life 15 for Myasthenia Gravis (QOL15-MG), and the Myasthenia Gravis Disability (MGDIS). We also assessed differences in MGII scores by disease severity with the ANOVA Kruskal-Wallis test. RESULTS: One hundred forty-one patients were enrolled. The mean MGII total score was 13.3 ± 11.9 (range 0-49), with a mean ocular subscore of 3.7 ± 4.7 and a mean MGII generalized subscore of 9.6 ± 9.0. As expected, the MGII had a good correlation with the other severity scores. The MGII had a lower floor effect (3.5%) than the other measures. Twenty-five patients were assessed in follow-up; as expected, the MGII change scores had moderate correlations with change in other MG severity measures and lower correlations with quality of life measures. CONCLUSIONS: The MGII score was cross-culturally validated in an Italian cohort of MG patients. We confirmed its lower floor effect and the correlations with other MG measures including INCB-MG that was not evaluated in previous studies.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".