EEG and MRI Abnormalities in Patients With Psychogenic Nonepileptic Seizures
Bibliographic record
Abstract
OBJECTIVE: To compare the rate of EEG and MRI abnormalities in psychogenic nonepileptic seizures (PNES) patients with and without suspected epilepsy. Patients were also compared in terms of their demographic and clinical profiles. METHODS: A retrospective analysis of 271 newly diagnosed PNES patients admitted to the epilepsy monitoring unit between May 2000 and April 2008, with follow-up clinical data collected until September 2015. RESULTS: One hundred ninety-four patients were determined to have PNES alone, 16 PNES plus possible epilepsy, 14 PNES plus probable epilepsy, and 47 PNES plus confirmed epilepsy. Fifty-seven of the 77 patients (74.0%) with possible, probable, or definite epilepsy exhibited epileptiform activity on EEG, versus only 16 of the 194 patients (8.2%) in whom epilepsy was excluded. Twenty-four of these 194 patients (12.4%) had MRI abnormalities. Three of 38 patients (7.9%) with both EEG and MRI abnormalities were confirmed not to have epilepsy. In PNES patients with EEG or MRI abnormalities compared with those without, patients with abnormalities were more likely to have epilepsy risk factors, such as central nervous system structural abnormalities, and less likely to report minor head trauma. The presence of EEG abnormalities in PNES-only patients did not influence antiseizure medication reduction, whereas those with MRI abnormalities were less likely to have their antiseizure medications reduced. CONCLUSIONS: Psychogenic nonepileptic seizure patients without MRI or EEG abnormalities are less likely to have associated epilepsy, risk factors for epilepsy, and had different demographic profiles. There is a higher-than-expected level of EEG and MRI abnormalities in PNES patients without epilepsy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".