Thymoma pathology and myasthenia gravis outcomes
Bibliographic record
Abstract
INTRODUCTION: There is limited evidence regarding the impact of World Health Organization (WHO) subtype of thymoma on post-thymectomy outcome of thymoma-associated myasthenia gravis (TAMG). The objective was to determine if the pathological subtypes of thymoma were associated with post-thymectomy outcomes of myasthenia gravis (MG), in patients with TAMG. METHODS: We performed a retrospective study of consecutive patients with TAMG who attended the neuromuscular clinic between January 2018 and December 2019 with a minimum follow-up of 1 y after thymectomy. Outcome measures were MG Impairment Index (MGII), single-simple question (SSQ), Myasthenia Gravis Foundation of America post-intervention status (MGFA PIS) and non-responder MG status at last assessment. RESULTS: Ninety-five patients were included; mean age at onset was 48.1 ± 12.1 y; 54(56.8%) were females. Thirteen patients developed MG post-thymectomy. The most common thymoma was WHO type B2 in 39 (41.1%). Most patients (40, 42.1%) had Masaoka stage II thymoma. There was no association of thymoma subtypes or Masaoka stage of disease with age, gender, MG phenotype, serology, post-thymectomy onset, interval from onset to thymectomy, MGII, SSQ, MGFA PIS, or non-responder status. Associations were found between positive serology and lower MGII (11.1 ± 14.2 vs 23 ± 12.9, P = .050), thymic follicular hyperplasia (TFH) and higher SSQ (89.3 ± 11.7 vs 80.1 ± 20.2, P-.043), and lack of recurrence and higher SSQ (84.1 ± 18 vs 72.5 ± 20, P = .037). DISCUSSION: The WHO pathological subtype of thymoma did not correlate with MG outcomes. However, positive acetylcholine antibody serology, presence of TFH, and non-recurrence of thymoma predict a favorable outcome.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".