Comparative Study of Trauma-Related Phenomena in Subjects With Pseudoseizures and Subjects With Epilepsy
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine potential differences in measures of trauma-related phenomena between subjects with pseudoseizures and subjects with intractable epilepsy. METHOD: Thirty-one adult subjects with pseudoseizures and 32 subjects with intractable epilepsy (confirmed by video-EEG) were recruited from the epilepsy unit of a tertiary care hospital. Each participant completed the Impact of Event Scale, the Davidson Trauma Scale, the Mississippi Scale for Combat-Related Posttraumatic Stress Disorder (PTSD), the Dissociative Experience Scale, and the Pittsburgh Sleep Quality Index, as well as demographic, seizure history, and family functioning measures. RESULTS: Subjects with pseudoseizures had significantly higher mean scores on the Davidson Trauma Scale, Mississippi Scale for Combat-Related PTSD, Impact of Event Scale, and Pittsburgh Sleep Quality Index than subjects with epilepsy. In addition, a significantly higher percentage of subjects with pseudoseizures had scores above the clinical cutoff level of 30 on the Dissociative Experience Scale. CONCLUSIONS: Subjects with pseudoseizures exhibited trauma-related profiles that differed significantly from those of epileptic comparison subjects and closely resembled those of individuals with a history of traumatic experiences. Interventions aimed at trauma-related issues may be beneficial for patients with pseudoseizures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".