Neuropsychological Predictors of Alexithymia in Psychogenic Nonepileptic Seizures and Epilepsy
Bibliographic record
Abstract
abstract: Alexithymia is a personality trait characterized by a diminished ability to identify and describe feelings, as well as an inability to distinguish physical symptoms associated with emotional arousal. Alexithymia is elevated in both patients with epilepsy (a neurologically-based seizure disorder) and psychogenic nonepileptic seizures (PNES; a psychological condition mimicking epilepsy); however, different neuropsychological processes may underlie this deficit in the two groups. To expand on previous research considering factors contributing to alexithymia in these populations, we examined the extent to which scores on the Toronto Alexithymia Scale (TAS-20) were predicted by performance on measures of executive and language functioning. We studied 138 PNES and 150 epilepsy patients with video-EEG confirmed diagnoses. Neuropsychological tests were administered to assess executive functioning (interference scores of the Stroop Color-Word Test and Part B of the Trail Making Test) and language functioning (Animals, Controlled Oral Word Association Test, and Boston Naming Test). Hierarchical linear regressions revealed that the relationships between disparate neuropsychological domains and alexithymia were not moderated by diagnosis of PNES or epilepsy. Multiple regression analyses within each group demonstrated that phonemic verbal fluency and response inhibition were significant predictors of alexithymia in epilepsy. Thus, alexithymia may reflect impairments in language and aspects of executive functioning in both PNES and 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".