Thymectomy For Non-Thymomatous Myasthenia Gravis: A Propensity Score Matched Study. (S36.004)
Bibliographic record
Abstract
Objective: To study the efficacy of thymectomy in achieving Remission or Minimal manifestation status in patients with non-thymomatous, generalized Myasthenia Gravis (MG). Background: The efficacy of thymectomy in patients with non-thymomatous MG is still unclear. Most studies have been limited due to no adjustment for confounders (e.g immunosuppressants), unclear definitions of remission and lack of control group. A randomized study is underway, but its results are not available yet. Methods: Patients with generalized MG and minimum follow-up of 6 months were included. Demographic data at onset, and treatments were recorded, as well as the MGFA post-intervention status at the last visit. Bayesian propensity score (PS) models were used to achieve a matched cohort of treated and untreated patients, balanced by age, sex, disease duration, severity at diagnosis and use of immunosuppressants. AChRAb status was excluded as not available in all patients. Cox proportional models were built to study treatment effects to achieve remission or minimal manifestation (Remission-MM) status. Results: 395 patients were identified. 183(46%) had a thymectomy. Thymectomy patients were younger (34.8 vs. 63.4 years, p<0.001), with more females (67.8% vs 41.9%, p<0.001) and more patients in MGFA classes 4&5 (21.8% vs 12.7%, p=0.01). A matched cohort (n=102) was created. The adjusted hazard ratio (HR) for the matched cohort was 1.43 (CI:1.32-1.54), and 1.51 (CI:0.8-2.84) for the unmatched cohort. The predicted Remission-MM rate was 16.6% in treated and 10.2% in controls at 5 years and 25.8% vs. 16.6% at 7 years. A Bayesian Cox model for the matched cohort had an estimated probability of efficacy (HR>1) of 96%. Discussion: In this cohort, thymectomy was associated with a higher probability of achieving Remission-MM status through time, controlling for several confounders. These results might not be applicable to a population radically different in age, severity or medications from the matched cohort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".