Seizures in <scp>anti‐Hu</scp>–associated extra‐limbic encephalitis: Characterization of a unique disease manifestation
Bibliographic record
Abstract
Anti-Hu-associated neurologic autoimmunity most often occurs in the context of small cell lung cancer and typically presents with peripheral neuropathy, cerebellar ataxia, and/or limbic encephalitis. Extra-limbic encephalitis causing seizures is a rare disease manifestation, with only sparse reports in the literature. Herein we present a patient with seizures in anti-Hu-associated extra-limbic encephalitis, and review the literature for other cases to more fully characterize this entity. Among 27 patients we identified, the median age was 46 years (range: 2-69 years) and 18 of 27 (67%) were female. Focal motor seizures were most common, followed by ictal expressive speech difficulty. Seizure semiologies along with neuroimaging findings most frequently suggested the involvement of the peri-Rolandic cortex, more anterior frontal operculum, and insula, although other cortical regions were rarely affected as well. In contrast to other classical paraneoplastic neurologic syndromes, good response to treatment with attainment of seizure-free survival was often reported, although over one-third still died. A propensity for chronic seizures among children indicated the potential to develop autoimmune-associated epilepsy. The predilection for certain extra-limbic regions, as well as the possibility of good response to treatment, may reflect unique disease mechanisms that would benefit from further study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".