Implementation and Outcome of Combining Interoceptive Exposure With Trauma-Related Exposure Therapy in a Patient With Combat-Related Posttraumatic Stress Disorder
Bibliographic record
Abstract
Theoretical considerations and several case studies suggest that trauma-related exposure therapy (TRE), which is one of the leading treatments for posttraumatic stress disorder (PTSD), may be augmented by adding interoceptive exposure (IE) therapy. The patient is a retired veteran with chronic PTSD as the primary (most severe) disorder and comorbid major depressive disorder. Treatment consisted of four sessions of IE followed by eight sessions of TRE. Structured interviews and self-report measures of psychopathology are administered pretreatment, midtreatment (after completing IE and before commencing TRE), posttreatment, and at 3-, 6-, and 12-month follow-ups. IE is associated with decreases in PTSD symptoms and anxiety sensitivity. At posttreatment, there are further reductions in PTSD symptoms and several associated symptoms. There are also further gradual improvements over the follow-up assessments. Implications of these findings are discussed, with an emphasis on identifying potential benefits and limitations of using IE+TRE for combat-related PTSD.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".