Adverse early-life experiences and epileptic seizures: the role of emotional dysregulation
Bibliographic record
Abstract
Introduction: There is clear evidence that adverse early-life experiences can lead to emotional dysregulation, significantly impairing individuals' quality of life. However, how these factors interact with each other in epileptic patients (Eps) has not yet been clarified. Methods: In this study, 89 individuals diagnosed with epilepsy completed standardized surveys, including the Difficulties in Emotion Regulation Scale (DERS), the Quality of Life in Epilepsy (QOLIE-31), the Toronto Alexithymia Scale (TAS-20), and the Adverse Childhood Experiences (ACE). A hierarchical multiple linear regression was run to determine the role of factors that were significant at a p ≤ .05 significance level in Pearson correlations in predicting the impact of seizures on quality of life. Subsequently, a mediation analysis was conducted. Results: The Strategies subscale of the DERS was found to mediate the relationship between the impact of epileptic seizures on quality of life and adverse early-life experiences. Discussion: These results showed that early-life adversities and lack of confidence in regulating emotions play a key role in the quality of life of Eps. These findings – albeit preliminary – may have clinical implications, guiding the psychological intervention programs that could be combined with medical treatments to mitigate emotion dysregulation and the consequences of early adverse experiences, as well as to improve the impact of epilepsy on quality of life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".