Misdiagnosis of Epileptic Seizures as Manifestations of Psychiatric Illnesses
Bibliographic record
Abstract
BACKGROUND: Epileptic seizures may be misdiagnosed if they manifest as psychiatric symptoms or seizures occur in patients with known psychiatric illness. METHODS: We present clinical profiles of six patients with epilepsy (three male, mean age 39 ± 12 years) that presented with prominent psychiatric symptoms. RESULTS: Two patients had pre-existing psychiatric illnesses. Three patients were initially diagnosed with panic attacks, two with psychosis, and one with schizophrenia. Five patients had temporal lobe epilepsy (TLE) while the sixth patient was subsequently found to have absence status epilepticus (SE). Cranial computed tomogram (CT) including contrast study was unremarkable in five patients and showed post-traumatic changes in one patient. Cranial magnetic resonance imaging (MRI) revealed dysembryoplastic neuroepithelial tumour (DNET) in one patient, cavernous hemangioma in one, and post-traumatic changes plus bilateral mesial temporal sclerosis in another patient but it was normal in two TLE patients. Routine electroencephalography (EEG) revealed absence SE in one patient but it was non-diagnostic in the TLE patients. Video-EEG telemetry in the epilepsy monitoring unit (EMU) was necessary to establish the diagnosis in four TLE patients. None of the patients responded to medications aimed at treating psychiatric symptoms alone. Two patients required surgery while the other four required treatment with anti-epileptic drugs. All the patients had favorable response to the treatment of their epilepsy. CONCLUSIONS: This case series illustrates that epileptic patients may experience non-convulsive seizures that might be mistaken as primary psychiatric illnesses. In this subset of patients, evaluation by an epileptologist, MRI of the brain, and/or video-EEG telemetry in an EMU was necessary to confirm the diagnosis of epilepsy if routine EEGs and cranial CT are normal.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".