Targeting the Endocannabinoid System in Psychiatric Illness
Bibliographic record
Abstract
Prevalence of psychiatric disorders continues to rise globally, yet remission rates and patient outcome remain less than ideal. As a result, novel treatment approaches for these disorders are necessary to decrease societal economic burden, as well as increase individual functioning. The recent discovery of the endocannabinoid system has provided an outlet for further research into its role in psychiatric disorders, because efficacy of targeted treatments have been demonstrated in medical illnesses, including cancers, neuropathic pain, and multiple sclerosis. The present review will investigate the role of the endocannabinoid system in psychiatric disorders, specifically schizophrenia, depressive, anxiety, and posttraumatic stress disorders, as well as attention-deficit hyperactivity disorder. Controversy remains in prescribing medicinal cannabinoid treatments due to the fear of adverse effects. However, one must consider all potential limitations when determining the safety and tolerability of cannabinoid products, specifically cannabinoid content (ie, Δ-tetrahydrocannabinol vs cannabidiol) as well as study design. The potential efficacy of cannabinoid treatments in the psychiatric population is an emerging topic of interest that provides potential value going forward in medicine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".