P.104 A portrait of generalized Myasthenia Gravis in Canada: analysis of the Adelphi MG II disease specific programme
Bibliographic record
Abstract
Background: Generalized myasthenia gravis (gMG) is a rare, chronic, autoimmune disease characterized by muscle weakness and fatigue. This study aims to describe the natural history, disease burden and treatment patterns of gMG patients in Canada. Methods: Data was analyzed from the Adelphi MG II DSP™, a gMG patient-level cross-sectional database, collected through surveys between February-June 2024. Neurologists provided sociodemographic, symptomatology, and treatments data. Results: Fifteen neurologists provided data for 46 gMG patients. The cohort’s mean (SD) age was 58.1 (14.7) years, 52.2% male, 82.6% White/Caucasian and 89.1% were anti-AChR Ab positive. Mean time since diagnosis was 3.4 (3.1) years, 22% reported a change in employment status due to gMG. Most had public insurance (68.9%). Disease severity was mostly MGFA class II (78.2%) patients. Common symptoms included eyelid ptosis (76.1%), dysarthria (50.0%), and dyspnea (54.3%) – mean MG-ADL was 5.6 (5.1). During their disease course, 34.9% experienced ≥1 myasthenic crisis, while 25.6% reported symptom exacerbation. At time of survey, patients had used 1.8 (0.9) lines of maintenance treatment. Most prescribed treatments (alone or in combinations) were pyridostigmine (95.6%), corticosteroids (48.9%), non-steroidal immunosuppressants (42.2%), Immunoglobulins (31.1%), and biologics (22.2%). Conclusions: gMG patients continue to experience symptoms burden and crisis/exacerbations. These findings highlight an unmet need for new, safe and effective therapeutics that are publicly covered to manage gMG-related clinical manifestations.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".