Neuropsychiatric profile in average intelligent individuals with coexisting epilepsy and psychogenic non‐epileptic seizures
Bibliographic record
Abstract
Global neuropsychological impairments with intellectual disability (ID) seem to play a major role in the occurrence of psychogenic non-epileptic seizures (PNES) in epilepsy. Conversely, the pathophysiology underlying PNES combined with epilepsy without ID remains elusive. We investigated the neuropsychiatric profile in 26 average intelligent subjects (15 women, mean age: 40.04 ± 13.53 years) with temporal lobe epilepsy (TLE) plus PNES (TLE + PNES), compared with 28 with TLE and 22 with PNES alone, matched for age and sex. All subjects underwent neuropsychiatric assessment, including Beck Depression Inventory-2 (BDI-2), State-Trait Anxiety Inventory (STAI), Dissociative Experiences Scale (DES), Toronto Alexithymia Scale (TAS-20), Traumatic Experience Checklist (TEC), and cognitive evaluation. TLE + PNES and PNES groups shared a similar psychiatric profile with higher levels of depression (BDI-2, P < 0.001), anxiety (STAI-S, P < 0.001; STAI-T, P < 0.001), dissociation (DES, P < 0.001), and alexithymia (TAS, P = 0.005) scales than the TLE group. Nonetheless, like individuals with TLE, patients with TLE + PNES had a lower rate of a potentially traumatizing event than PNES. The very low rate of potentially traumatizing event in subjects with TLE + PNES leads us to hypothesize that epilepsy itself may be the psychophysiological distress that contributed to PNES. A psychopathological assessment in subjects with epilepsy is crucial to identify those more likely to develop PNES.
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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 | 0.001 |
| 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 | 0.000 |
| 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".