L'évolution des indications thérapeutiques du cannabis : analyses clinique et réglementaire à travers les essais cliniques internationaux
Bibliographic record
Abstract
This thesis examines the evolution of therapeutic cannabis (Cannabis sativa) indications over time, based on data from European and global databases such as EudraCT and ClinicalTrials.gov. Research shows a significant increase in clinical trials aimed at evaluating the efficacy of cannabis for various medical conditions. In neurology, cannabis, particularly cannabidiol (CBD), has shown promising results in managing Lennox-Gastaut and Dravet syndromes, as well as multiple sclerosis. Studies reveal that CBD can reduce the frequency and severity of epileptic seizures, offering a new therapeutic option for patients resistant to conventional treatments. In oncology, clinical trials have explored the use of Sativex® for pain management in patients with advanced cancer. While preliminary results are promising, further research is needed to validate these benefits. In psychiatry, studies on therapeutic cannabis have yielded mixed results. Some research indicates potential benefits, particularly for trichotillomania and neuropsychiatric symptoms related to dementia, but other studies have not met their primary endpoints, highlighting the need for continued investigation. Regulatory frameworks in Germany, Israel, and Canada have facilitated rigorous clinical research and patient access to medical cannabis. This thesis concludes that, although therapeutic cannabis shows notable potential, it is crucial to continue research to refine dosages, formulations, and appropriate indications, while ensuring the safety of treatments.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".