Le cannabidiol comme traitement d’appoint de la dépression bipolaire aiguë : une étude pilote
Bibliographic record
Abstract
Objective The treatment of bipolar depression remains challenging due to the limited effective and safe therapeutic options available; thus, developing newer treatments that are effective and well tolerable is an urgent unmet need. The objective of the present trial was to test 150 to 300 mg/day of cannabidiol as an adjunctive treatment for bipolar depression. Method A randomized, double-blind, placebo-controlled pilot study to assess the efficacy of adjunctive cannabidiol in bipolar depression was used. Efficacy parameters were changes in the Montgomery-Åsberg Depression Rating Scale (MADRS) from baseline to week 8. Secondary outcomes included response and remission rates, changes in anxiety and psychotic symptoms, and changes in functioning. Patients continued double-blind treatment until week 12 to monitor for adverse effects, laboratory analysis, and manic symptoms. Study registry: NCT03310593. Results A total of 35 participants were included. MADRS scores significantly decreased from baseline to the endpoint (placebo, −14.56; cannabidiol, −15.38), but there was no significant difference between the groups. Similarly, there were no other significant effects on the secondary outcomes. However, an exploratory analysis showed a significant effect of cannabidiol 300 mg/day in reducing MADRS scores from week 2 to week 8 (placebo, −6.64; cannabidiol, −13.72). There were no significant differences in the development of manic symptoms or any other adverse effects. Conclusion Cannabidiol did not show significantly higher adverse effects than placebo. Despite the negative finding on the primary outcome, an exploratory analysis suggested that cannabidiol should be further studied in bipolar depression in higher doses of at least 300 mg/day and under research designs that could better control for high placebo response.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".