Does the addition of cognitive therapy to exposure and response prevention for obsessive compulsive disorder enhance clinical efficacy? A randomized controlled trial in a community setting
Bibliographic record
Abstract
OBJECTIVES: Exposure and response prevention (ERP) remains the most empirically supported psychological treatment for obsessive compulsive disorder (OCD). Clinical guidelines recommend the addition of cognitive approaches to ERP although the presumed additive benefits have not been directly tested. The aim of this was to compare a treatment that integrated cognitive therapy with ERP (ERP + CT) to traditional, manualized ERP to test the additive benefits. DESIGN: A longitudinal, randomized control trial design was used. METHODS: Participants (N = 127) with OCD were randomly assigned to receive individual outpatient ERP or ERP + CT. Obsessive-compulsive symptom severity measures were completed pre- and post-treatment and at 6-month follow-up. RESULTS: While both conditions led to significant symptom and obsessive belief reduction, ERP + CT led to significantly greater symptom and belief reduction as compared to ERP across all main symptom presentations of OCD. Based on a priori definitions of effectiveness, more patients in ERP + CT compared to the ERP group were also deemed treatment responders. CONCLUSIONS: The results of this study suggest that cognitive therapy can be readily integrated with ERP to improve clinical outcomes beyond ERP alone. PRACTITIONER POINTS: Both ERP and ERP + CT were effective, however a course of ERP + CT was significantly more effective at reducing symptoms of OCD than the ERP treatment condition. Significantly more participants who received ERP + CT experienced clinically significant change in OCD symptoms compared to those who received ERP. OCD symptom dimension did not significantly impact response to either ERP or ERP + CT treatments.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".