Changes in patient health questionnaire (PHQ-9) scores in adults with medical authorization for cannabis
Bibliographic record
Abstract
BACKGROUND: Legal access to medical cannabis is increasing world-wide. Despite this, there is a lack of evidence surrounding its efficacy on mental health outcomes, particularly, on depression. This study assesses the effect of medical cannabis on Patient Health Questionnaire (PHQ-9) scores in adult patients between 2014 and 2019 in Ontario and Alberta, Canada. METHODS: An observational cohort study of medically authorized cannabis patients in Ontario and Alberta. Overall change in PHQ-9 scores from baseline to follow-up were evaluated (mean change) over a time period of up to 3.2 years. RESULTS: 37,338 patients from the cohort had an initial PHQ-9 score recorded with 5103 (13.7%) patients having follow-up PHQ-9 scores. The average age was 54 yrs. (SD 15.7), 46% male, 50% noted depression at baseline. The average PHQ-9 score at baseline was 10.5 (SD 6.9), following a median follow-up time of 196 days (IQR: 77-451) the average final PHQ-9 score was 10.3 (SD 6.8) with a mean change of - 0.20 (95% CI: - 0.26, - 0.14, p-value < 0.0001). Overall, 4855 (95.1%) had no clinically significant change in their PHQ-9 score following medical cannabis use while 172 (3.4%) reported improvement and 76 (1.5%) reported worsening of their depression symptoms. CONCLUSIONS: Although the majority showed no clinically important changes in PHQ-9 scores, a number of patients showed improvement or deteriorations in PHQ-9 scores. Future studies should focus on the parallel use of screening questionnaires to control for PHQ-9 sensitivity and to explore potential factors that may have attributed to the improvement in scores pre- and post- 3-6 month time period.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".