Functional Seizures in the Elderly: Accurate Diagnosis Can Reduce Iatrogenic Harm
Bibliographic record
Abstract
An 87-year-old man with multiple vascular risk factors presented to the emergency department four times in eighteen months with new, recurrent paroxysmal events, without loss of awareness and able to recall the events.At the onset of the events, he felt that "the world changed around [him]," and everything seemed far away.He then felt a pulling sensation in his right face, and right arm and leg heaviness.He was unable to get words out, despite knowing what he wanted to say, with involuntary vocalization instead.These episodes typically lasted 20 to 60 minutes, although some were much shorter lasting only minutes, after which he felt tired, but not confused.This description was corroborated by witnesses.Since onset, these events were occurring in clusters every 3-4 months.During the first event, an urgent computed tomography (CT)/CT angiogram of the head and neck revealed occlusion of the right internal carotid artery (ICA) and 50% stenosis of the left ICA, without acute abnormality.He was treated with tissue plasminogen activator, and his symptoms resolved over the following hour, but with recurrent episodes during admission.Magnetic resonance imaging (MRI) of the brain showed a remote right caudate nucleus infarct, without acute infarction.CT perfusion performed during one of the events did not demonstrate any abnormalities.Electroencephalogram (EEG) was normal, but an event was not captured.Given the repeated events in hospital without imaging features of acute stroke, a provisional diagnosis was made of non-lesional focal onset epilepsy with focal aware cognitive seizures, and he was started on levetiracetam 750 mg twice daily.He presented to the emergency department 15 months later with a recurrent, prolonged event and was admitted to inpatient neurology.All investigations were unremarkable including MRI, EEG, blood work with paraneoplastic and autoimmune encephalitis antibodies, echocardiogram, and Holter monitor.He continued to have events in hospital, despite escalation of antiseizure medications.At discharge, he was taking levetiracetam 3000 mg daily, valproic acid 1000 mg daily, and clobazam 30 mg daily.He was re-admitted shortly after with delirium and falls, presumed secondary to antiseizure medications.He was weaned
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".