Subcutaneous Immunoglobulin Therapy in the Chronic Management of Myasthenia Gravis: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Immunoglobulin therapy has become a major treatment option in several autoimmune neuromuscular disorders. For patients with Myasthenia Gravis (MG), intravenous immunoglobulin (IVIg) has been used for both crisis and chronic management. Subcutaneous Immunoglobulins (SCIg), which offer the advantage of home administration, may be a practical and effective option in chronic management of MG. We analyzed clinical outcomes and patient satisfaction in nine cases of chronic disabling MG who were either transitioned to, or started de novo on SCIg. METHODS AND FINDINGS: This was a retrospective cohort study for the period of 2015-2016, with a mean follow-up period of 6.8 months after initiation of SCIg. All patients with MG treated with SCIg at the Ottawa Hospital, a large Canadian tertiary hospital with subspecialty expertise in neuromuscular disorders were included, regardless of MG severity, clinical subtype and antibody status. The primary outcome was MG disease activity after SCIg initiation. This outcome was measured by 1) the Myasthenia Gravis Foundation of America (MGFA) clinical classification, and 2) subjective scales of disease activity including the Myasthenia Gravis activities of daily living profile (MG-ADL), Myasthenia Gravis Quality-of-life (MG-QOL 15), Visual Analog (VA) satisfaction scale. We also assessed any requirement for emergency department visits or hospitalizations. Safety outcomes included any SCIg related complication. All patients were stable or improved for MGFA class after SCIg initiation. Statistically significant improvements were documented in the MG-ADL, MG-QOL and VAS scales. There were no exacerbations after switching therapy and no severe SCIg related complications. CONCLUSIONS: SCIg may be a beneficial therapy in the chronic management of MG, with favorable clinical outcome and patient satisfaction results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".