Coexistence of Lambert–Eaton myasthenic syndrome and autoimmune encephalitis with anti-CRMP5/CV2 and anti-GABAB receptor antibodies in small cell lung cancer
Bibliographic record
Abstract
RATIONALE: Autoimmune encephalitis and Lambert-Eaton myasthenic syndrome are classic paraneoplastic neurological conditions common in patients with small cell lung cancer. PATIENT CONCERNS: The patient complained of tiredness, fluctuating recent memory loss, and inability to find his home. His family members reported a change in character, irritability, and paranoia. One month later, the patient had 1 grand mal seizure lasting 5 minutes. DIAGNOSIS: The patient was diagnosed with limbic encephalitis combined with Lambert-Eaton myasthenic syndrome. The gamma-aminobutyric acid B (GABAB) receptor and collapsin response mediator protein 5 (CRMP5, also called CV2) antibody test results were positive. Nine months after the onset of symptoms, the patient was diagnosed with small cell lung cancer. INTERVENTIONS: The patient was administered intravenous immunoglobulin for 5 days. He was then treated with 60 mg prednisone once per day. The prednisone dose was gradually reduced by 1 tablet every 2 weeks. After the diagnosis, the patient underwent 6 courses of chemotherapy with cisplatin combined with sequential chemoradiation therapy. OUTCOMES: The patient was able to take care of himself. Neurological examination revealed a lower limb proximal muscle strength level of 4 and a reduced limb tendon reflex. The patient had deficits in short-term memory, a Mini-Mental State Examination score of 26, Montreal Cognitive Assessment score of 24, Self-rating Depression Scale score of 54 (mild depression), and Self-Rating Anxiety Scale score of 42 (normal). LESSONS: Autoimmune diseases of the peripheral and central nervous systems can be observed at the same time in patients with small cell lung cancer, even when magnetic resonance imaging findings are negative and immune therapy is effective.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".