The use of psychotropic agents for the symptoms of obsessive–compulsive disorder
Bibliographic record
Abstract
OBJECTIVE: To describe the use of psychotropic agents in a sample of subjects with obsessive-compulsive disorder (OCD), and in particular the differences associated with different OCD symptoms. METHOD: A total of 154 subjects participated in a study assessing OCD symptom subtypes, called the Nepean OCD Study. In addition to a comprehensive evaluation of the subjects' OCD symptoms using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), Vancouver Obsessive-Compulsive Inventory (VOCI), and the Sheehan Disability Scale (SDS), the subjects' medication history was recorded. The association between symptom severity, disability, OCD symptom subtypes and the use of psychotropic agents was examined. RESULTS: Psychotropic medication was taken by 93 (60.4%) participants. In the majority of cases (n=55, 59.1%), selective serotonin reuptake inhibitors (SSRIs) were taken, and of the SSRIs, the most commonly used agent was escitalopram (n=21, 22.6%). Psychotropic agents were more likely to be taken by subjects with higher Y-BOCS and SDS scores. Hoarding was associated with a lower likelihood of psychotropic use, whereas unacceptable/taboo thoughts were associated with an increased likelihood of psychotropic and antipsychotic use. CONCLUSION: Patients with OCD are more likely to be taking psychotropic agents if they have a more severe illness, greater disability and more prominent unacceptable/taboo thoughts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".