Firefighter With Co-Morbid Psychogenic Non-Epileptic Seizures and Post-Traumatic Stress Disorder Treated With Prolonged Exposure Therapy: Long-Term Follow-Up
Bibliographic record
Abstract
Psychogenic non-epileptic seizures (PNES), are events that resemble epileptic seizures but lack electrophysiological or clinical evidence for epilepsy. Instead, they are psychogenic in origin. These episodes tend to occur with alterations in consciousness and bodily functions and are the result of mechanisms of conversion. Psychological trauma and post-traumatic stress disorder (PTSD) are prevalent among patients with PNES. This is a case report of a 32-year-old male who began treatment 1-year after developing PTSD followed some months later by PNES. His seizures were characterized by contorted movements of the head and neck, guttural sounds, and left sided movements or whole-body arching and were accompanied by frequent falls and injuries. They were usually brief but occurred daily. Psychotherapy had been discontinued because violent seizures often interrupted the sessions. He was treated with prolonged exposure (PE) at a PNES program and by the last session, had achieved an improvement in his seizure frequency (one every 4–6 days rather than daily episodes). This allowed him to begin therapy with a local therapist. Two years after completing treatment, the patient returned for a follow up visit. At that point, his seizure frequency, was one per month which shows he sustained and improved on this symptom. Former head drops, and grunting sounds disappeared, and he was no longer using a cane to ambulate. From an emotional standpoint (PTSD, suicidality, anxiety, quality of life), the patient had achieved and maintained a much healthier level of functioning (though no change on alexithymia, anger, depression, and trait anxiety).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".