Clinical and Neuropsychiatric profiles of patients with Psychogenic Non-epileptic Seizures (PNES) and Epilepsy (P5.092)
Bibliographic record
Abstract
BACKGROUND The lifetime experiences of patients with epilepsy are similar to those of patients with PNES, but the etiology of their seizures and psychological and psychiatric make-up are different. PNES often lead to substantial and repetitive utilization of emergency medical services, iatrogenic harm, and overall increased cost of healthcare. We sought to conduct a comparison of neuropsychiatric profiles of patients diagnosed with PNES and epilepsy patients to better understand the differences between the groups. DESIGN/METHODS In addition to collecting demographic and clinical data, 192 patients prospectively filled out 8 self-paced questionnaires: Toronto Alexithymia Scale (TAS), Barratt Impulsivity Scale (BIS), SOM-7, Personality Assessment Inventory (PAI), Childhood trauma Questionnaire (CTQ), Quality of Life in Epilepsy (QOLIE-89) and BDI-II. Independent-samples t-tests (or Chi-square) were conducted to compare these groups. RESULTS 62 patients were diagnosed with PNES and 69 with epilepsy. Patients with PNES tended to have less full-time employment (p=0.057), are more likely to carry a psychiatric diagnosis (p=0.001), and are more likely to have a psychiatrist or therapist (both p<0.05). Neuropsychiatric measures revealed higher SOM-7 (p<0.001), lower QOL scores (p=0.045) and higher scores on PAI somatic complaints scale (p=0.004) in patients with PNES. CONCLUSIONS Although patients with PNES tend to be similar to patients with epilepsy in their clinical symptomatology, they have a higher number and severity of somatic complaints and lower quality of life. Further investigations into the reasons for these differences are warranted in order to develop better treatment strategies for patients with 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) | 0.003 | 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".