An update on the Pharmacological and Psychotherapeutic Management of Treatment Resistant OCD
Bibliographic record
Abstract
Abstract Although the rates of response to first-line pharmacological treatments (serotonin reuptake inhibitors – SRIs) are generally twice that of placebo, only 40-60% of patients respond sufficiently or are able to tolerate traditional first-line pharmacotherapy. Augmentation with dopamine antagonist medications is associated with the strongest evidence, yet dopamine antagonists benefit only a minority of those who try them and carry elevated risks of adverse effects. Based on evolving pathophysiologic models of OCD, a variety of agents targeting serotonin, dopamine, norepinephrine, glutamate, and anti-inflammatory pathways have been explored as alternative or adjunctive therapies for treatment-resistant OCD and have at least preliminary evidence of efficacy. Similarly, approximately 50% of patients do not respond optimally to first psychotherapeutic treatments including cognitive behavioural therapy (CBT) and exposure and response prevention (ERP), even when combined with pharmacotherapy. The psychotherapy outcome literature is heterogeneous and very few psychological strategies have been developed specifically to treat treatment resistant OCD. However, a recent systematic review concluded that CBT improved treatment response in individuals with pharmacotherapy resistance. This presentation will present an update on the pharmacological and psychotherapeutic treatment of refractory OCD including novel strategies such as the use of psychedelics. Disclosure of Interest M. Van Ameringen Grant / Research support from: Elvium, CIHR,Hamilton Academic Health Sciences Organization, Consultant of: Abbvie, Bausch Health, Boehringer Ingelheim, Elvium, Jazz, Vistagen, Speakers bureau of: Abbvie, Elvium (Purdue), Lundbeck, Otsuka, Sunovion, and Takeda
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".