Comparison of Alexithymia, Perceived Stress and Emotion Regulation Between Patients with Epileptic and Non-Epileptic Seizures
Bibliographic record
Abstract
Background: Different psychological factors may explain the differences between epileptic and non-epileptic seizures. Accordingly, the present study was conducted to compare alexithymia, perceived stress, and emotion regulation between patients with epileptic and non-epileptic seizures. Methods: In the present cross-sectional research, 82 patients (39 with epileptic seizures and 43 with non-epileptic seizures) were selected. Using the Toronto Alexithymia Scale (TAS) (Bagby et al., 1994), Perceived Stress Scale (PSS) (Cohen et al., 1983) and cognitive emotion regulation questionnaire (CERQ) (Garnefski and Kraaij, 2006), the required data were collected and then analyzed using the SPSS-19 software. Results: According to the results, patients with non-epileptic seizures had significantly higher scores in TAS, especially in the subscales of difficulty identifying feelings and externally oriented thinking, PSS and CERQ, especially in the subscales of catastrophizing and other-blame and lower scores in CERQ’s subscales of acceptance, positive reappraisal, and positive refocusing compared to patients with epileptic seizures (P < 0.05). The non-epileptic seizure group score was higher in the event acceptance subscale (as a positive emotion regulation) (P < 0.05). Conclusions: The results of this study showed that patients with epileptic seizures have a more favorable condition in terms of alexithymia, emotion regulation, and perceived stress compared to those with non-epileptic seizures.
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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 | 0.000 |
| 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) | 0.001 | 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".