Thymectomy may not be associated with clinical improvement in MuSK myasthenia gravis
Bibliographic record
Abstract
INTRODUCTION: A randomized trial demonstrated benefit from thymectomy in nonthymomatous acetylcholine receptor (AChR)-antibody positive myasthenia gravis (MG). Uncontrolled observational and histologic studies suggest thymectomy may not be efficacious in anti-muscle-specific kinase (MuSK)-MG. METHODS: The therapeutic impact of thymectomy was evaluated from data collected for a multicenter, retrospective blinded review of rituximab in MuSK-MG. RESULTS: Baseline characteristics were similar between thymectomy (n = 26) and nonthymectomy (n = 29) groups, including treatment with rituximab (42% vs. 45%). At last visit, 35% of thymectomy subjects reached the primary endpoint, a Myasthenia Gravis Foundation of America (MGFA) post-intervention status (PIS) score of minimal manifestations (MM) or better, compared with 55% of controls (P = 0.17). After controlling for age at onset of MG, rituximab, prednisone, and intravenous immunoglobulin/plasma exchange treatment, thymectomy was not associated with greater likelihood of favorable clinical outcome (odds ratio = 0.43, 95% confidence interval 0.12-1.53, P = 0.19). DISCUSSION: Thymectomy was not associated with additional clinical improvement in this multicenter cohort of MuSK-MG patients. Muscle Nerve 59:404-410, 2019.
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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 | 0.002 | 0.005 |
| 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) | 0.003 | 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".