Interoceptive Exposure Therapy Combined with Trauma‐related Exposure Therapy for Post‐traumatic Stress Disorder: a Case Report
Bibliographic record
Abstract
Trauma-related exposure therapy is a useful but not universally effective treatment for post-traumatic stress disorder. Anxiety sensitivity may play an important role in this disorder, as it does in panic disorder. Studies have shown that interoceptive exposure therapy reduces anxiety sensitivity in panic disorder. The present case study was a preliminary investigation of the merits of including interoceptive exposure therapy in the treatment of post-traumatic stress disorder, in order to improve treatment outcome for a patient who had no history of panic disorder or panic attacks. Interoceptive exposure therapy (4 sessions) was one component of treatment, combined with trauma-related exposure therapy (4 sessions of imaginal exposure followed by 4 sessions of in vivo exposure). Treatment outcome was assessed with the Clinician-Administered Post-traumatic Stress Disorder Scale, a self-report measure of post-traumatic stress disorder symptoms, and measures of symptoms and cognitions commonly associated with post-traumatic stress disorder. Scores on all outcome measures decreased over the course of treatment, with gains maintained at 1- and 3-month follow-up. Symptoms of anxiety sensitivity and post-traumatic stress disorder decreased during interoceptive exposure therapy. The results indicate that interoceptive exposure therapy is a promising adjunctive intervention for post-traumatic stress disorder. Further research is needed into the merits of combining interoceptive exposure therapy and trauma-related exposure therapy as a means of boosting treatment efficacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".