Pharmacological treatment strategies in obsessive compulsive disorder: A cross-sectional view in nine international OCD centers
Bibliographic record
Abstract
OBJECTIVE: It is unknown what next-step strategies are being used in clinical practice for patients with obsessive-compulsive disorder (OCD) who do not respond to first-line treatment. As part of a cross-sectional study of OCD, treatment and symptom information was collected. METHOD: Consecutive OCD out-patients in nine international centers were evaluated by self-report measures and clinical/structured interviews. OCD symptom severity was evaluated by the Yale Brown Obsessive Compulsive Scale (YBOCS) and Clinical Global Impression-Severity Scale (CGI-S). Clinical response to current treatment was evaluated by the CGI-Improvement Scale (CGI-I ≤ 2). RESULTS: In total, 361 participants reported taking medication; 77.6% were taking a selective serotonin reuptake inhibitor; 50% reported use of at least one augmentation strategy. Antipsychotics were most often prescribed as augmenters (30.3%), followed by benzodiazepines (24.9%) and antidepressants (21.9%). No differences in OCD symptom severity were found between patients taking different classes of augmentation agents. CONCLUSIONS: Results from this international cross-sectional study indicate that current OCD treatment is in line with evidence-based treatment guidelines. Although augmentation strategies are widely used, no significant differences in OCD symptom severity were found between monotherapy and augmentation or between different therapeutic agents.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".