New treatments for OCD? Evidence for cannabinoids and psychedelics
Bibliographic record
Abstract
The etiology of OCD is complex and appears to involve multiple biological pathways. Imbalances in central serotonin, dopamine, and glutamate activities are widely thought to play a causative role. Despite strong evidence supporting first-line OCD pharmacotherapies, approximately 40-60 % of OCD patients remain unresponsive and are considered treatment resistant (TR). Although a range of agents have been examined in TR-OCD, there is no gold-standard, indicating a need to broaden our clinical armamentarium. Cannabis has been used for centuries in many cultures for both medicinal and recreational purposes. Clinical interest in these agents has recently re-emerged. The current evidence for the use of cannabinoids in OCD is very small and includes survey-based, self-report studies with very few controlled trials. Additionally, after a long hiatus from psychiatric research, psychedelics have re-emerged as agents of interest within the past decade. A comprehensive scoping review of the OCD literature including published and grey literature was conducted and detailed in this paper. The current evidence associated with Cannabinoids, Psilocybin, Lysergic acid diethylamide (LSD), N,N-Dimethyltryptamine (N,N-DMT), and Methylenedioxyphenethylamine (MDMA) in the treatment of OCD is detailed. Much of the current evidence examining cannabinoids and psychedelics in OCD is from cross-sectional surveys and case reports, as well as some small clinical trials. There is a shortage of well-controlled, methodologically rigorous RCTs to properly test the efficacy of cannabinoids or psychedelics in OCD and related disorders. However, the current evidence appears to indicate a lack of evidence supporting the use of either synthetic or natural cannabinoids to treat OCD, but a stronger signal for the use of psilocybin in TR-OCD.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".