Bibliographic record
Abstract
Many (though not all) of my patients who have tried marijuana have felt that their tics improved after using it. Such self-treatment is not rare (poster P94 here), and other doctors report similar results (see for example poster P6 here). Pharmacological benefits from cannabis products are plausible, since cannabinoid receptors in the brain's basal ganglia are well positioned to affect movement . Of course, in addition to any real benefit from marijuana, there could be expectation effects, or one could simply care less about tics when high. Random allocation clinical trials with blind rating of benefit (RCTs) are essential to demonstrating whether marijuana has any true benefit for tics. Müller-Vahl and colleagues carried out two RCTs about 15 years ago in Tourette syndrome (TS) using THC (tetrahydrocannabinol), the main intoxicating ingredient in cannabis . Both trials showed benefit, but the trials were relatively small. Two to 3 years ago, the Tourette Association of America funded two pilot studies in this field, but results have not yet been reported. One trial, at Yale, was to study the FAAH (fatty acid amide hydrolase) inhibitor PF-04457845 in TS , but the trial was placed on clinical hold pending results from a different trial. Investigators at Toronto Western Hospital were funded for a trial in TS of medical cannabis products with varying concentrations of THC and cannabidiol . Cannabidiol is being studied in several brain disorders, including epilepsy, with hopes that it may provide benefit without the psychological side effects of THC. Not surprisingly, the paucity of data has led to different viewpoints. Müller-Vahl has argued that THC may be appropriate in some TS patients , whereas an American Academy of Neurology review and a Cochrane-style review in JAMA concluded that the evidence was insufficient to recommend THC for tic disorders . The clinical utility of cannabinoids in TS was one of two clinical controversies debated at the 2015 First World Congress on Tourette Syndrome and Tic Disorders .
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.128 | 0.040 |
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".