A Case of Good Syndrome Presumed Secondary to Metastatic Pancreatic Thymoma in a Patient Presenting With a Myasthenic Crisis Postthymectomy
Bibliographic record
Abstract
INTRODUCTION: Myasthenia gravis (MG) is an autoimmune disorder characterized by autoantibodies against the postsynaptic nicotinic acetylcholine receptors, muscle-specific tyrosine kinase, low-density lipoprotein receptor-related protein 4, and agrin. The incidence of thymoma in MG is reported as ∼10%-15%. The incidence of extrathoracic metastatic thymoma is exceedingly rare and may present years after resection. Associations between thymoma and immunodeficiency have also been described, including Good syndrome. METHODS AND RESULTS: We describe the clinical course, investigations, and treatments performed in a patient presenting with a myasthenic crisis in the setting of acetylcholine receptor antibody-positive generalized MG 10 years postthymectomy. Computed tomography imaging revealed 2 pancreatic lesions, but no residual thoracic thymoma. Biopsy confirmed metastatic pancreatic thymoma, which was successfully resected. His course was further complicated by cytomegalovirus retinitis with a depressed CD4 count and perniosis. DISCUSSION: This presentation was felt to be consistent with Good immunodeficiency syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".