A.01 Relieving the burden of myasthenia gravis: eculizumab reduces exacerbation, hospitalization and rescue therapy rates
Bibliographic record
Abstract
Background: Patients with anti-acetylcholine receptor antibody-positive (AChR+) generalized myasthenia gravis (MG) unresponsive to conventional treatment experience greater disease burden than responsive patients. This is partly due to exacerbations, which may result in significant healthcare resource utilization. Eculizumab is well tolerated and gives clinically meaningful benefits in these patients. We evaluated the effect of long-term eculizumab treatment on exacerbations, hospitalizations and rescue therapy in the REGAIN study and its open-label extension. Methods: Exacerbations were defined as clinical worsening/deterioration, MG crises or rescue therapy usage; pre-study exacerbations/hospitalizations were defined from patient records. Event rates adjusted for patient-years were calculated for all patients in the pre-study year, patients receiving placebo during REGAIN, and patients receiving eculizumab during REGAIN and its open-label extension (median exposure, 27.5 months [range, 22 days–42.8 months]); rates were compared using a Poisson regression model. Results: Eculizumab treatment reduced exacerbations by 65% (p=0.0057), hospitalizations by 71% (p=0.0316) and rescue therapy use by 66% (p=0.0072) versus placebo. Eculizumab treatment reduced exacerbations by 74% and hospitalizations by 83% (both p<0.0001) versus the pre-study year. Conclusions: Long-term eculizumab treatment reduces disease burden and healthcare resource utilization, demonstrating continuing improvements in clinical endpoints that lead to additional meaningful outcomes for patients with AChR+ generalized MG. (NCT01997229, NCT02301624).
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".