Clinical profile and impact of comorbidities in patients with very‐late‐onset myasthenia gravis
Bibliographic record
Abstract
INTRODUCTION/AIMS: The purpose of this study was to evaluate the clinical profile of myasthenia gravis (MG) in older patients and determine the impact of medical comorbidities on their MG status and outcome. METHODS: This was a retrospective chart review of patients with a symptom onset of MG at or after 65 years of age. Correlations were made between demographics, clinical characteristics, the Myasthenia Gravis Foundation of America (MGFA) severity scale scores, and Myasthenia Gravis Impairment Index (MGII) scores with two outcome measures: MGFA Post-Intervention Status (MGFA-PIS) and Simple Single Question (SSQ). RESULTS: The study population included 109 patients, with 90 of them having more than one follow-up visit. Their mean age was 75.3 ± 6.9 years and sex distribution was even. Of these patients, 67.7% had generalized MG. Nine-one percent of patients had one comorbidity. None of the demographic factors or comorbidities showed an association with MGFA-PIS, SSQ, or MGII after correction for multiple comparisons. Seventy-one percent of the patients improved with treatment, 12.4% remained unchanged, and 16.6% showed worsening at their last follow-up visit. DISCUSSION: Our study shows that patients with very-late-onset MG had a good prognosis and treatment response. None of the comorbidities had an impact on the severity of myasthenic symptoms or on outcome in these patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".