Cognitive behavioural and neuropsychiatric treatment of post‐traumatic conversion disorder: a case study
Bibliographic record
Abstract
Conversion disorder consists of involuntary sensory or motor symptoms and deficits that cannot be explained by a general medical condition. There are several treatment options, although none has emerged as the treatment of choice. The present case study examined the effects of adding cognitive behaviour therapy to neuropsychiatric management of conversion disorder (motor subtype). The patient, a retired emergency services worker, presented with a history of intermittent episodes of speech disruption (inability to speak or difficulty speaking properly). Although episodes of speech disturbance sometimes occurred unexpectedly, they were more likely to occur under conditions of stress and fatigue, and were triggered by reminders of work-related traumatic events. The patient was treated with pharmacotherapy and psychoeducation from a neuropsychiatrist. With the aim of improving treatment outcome, cognitive behaviour therapy was added, involving imaginal exposure to trauma memories, along with cognitive restructuring. The frequency of between- and within-session speech disturbance episodes declined over the course of cognitive behaviour therapy to the point that the patient was essentially symptom-free. Within-session distress ratings also decreased, which suggested habituation to trauma-related memories. This case study demonstrates how particular cognitive behaviour therapy interventions can be usefully applied to one form of conversion disorder.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".