Augmenting serotonin reuptake inhibitors in obsessive–compulsive disorder: What moderates improvement?
Bibliographic record
Abstract
OBJECTIVE: Patients with obsessive-compulsive disorder (OCD) often only partially respond to serotonin reuptake inhibitors (SRIs). In such cases, American Psychiatric Association practice guidelines suggest augmenting SRIs with cognitive-behavioral therapy consisting of exposure and ritual prevention (EX/RP) or antipsychotic medication (i.e., risperidone). We examined moderators and predictors of these 2 augmentation strategies. METHOD: Data came from a randomized controlled trial that compared adding EX/RP or risperidone to SRIs in adults with OCD. Patients entered the study on a stable SRI dosage and were randomized to EX/RP (N = 40), risperidone (N = 40), or placebo (N = 20). Data were analyzed using multilevel modeling. RESULTS: Pretreatment OCD severity, age, and depression were significant moderators. Although OCD severity was unrelated to EX/RP response, individuals with more severe OCD had poorer outcomes and slower improvement with risperidone. Increasing age predicted better response to risperidone, but not EX/RP. Increased depression predicted poorer response to placebo, but not EX/RP or risperidone. Poorer functioning predicted worse outcome across all 3 conditions. Together, these moderators and predictor accounted for 33% of the variance in outcomes, above and beyond the 30.8% accounted for by treatment condition. CONCLUSIONS: SRI augmentation with EX/RP was more effective than risperidone across all of the demographic and clinical variables tested. EX/RP's superiority over risperidone increased with baseline OCD severity and with younger age. These data indicate that EX/RP should be the recommended SRI augmentation strategy, even for severe OCD. What determines the degree of EX/RP response in individual patients deserves further study.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".