Cannabis use in patients with insomnia and sleep disorders: Retrospective chart review
Bibliographic record
Abstract
Background: Medical cannabis has been increasingly used in Canada after being sanctioned by Health Canada in 2001. Insomnia and sleep disorders are among the most common conditions for which patients report using cannabis. Current research shows cannabis may have a beneficial effect in sleep disorders and may improve patient-reported sleep scores. Methods: A retrospective chart review was conducted at Hybrid Pharm community pharmacy in Ottawa, Ontario, and included patients who were interested in, or already using, medical cannabis for sleep disorders. A qualitative, exploratory approach was taken to evaluate the descriptive efficacy and safety of medical cannabis when prescribed for insomnia or comorbid conditions. The comprehensive data collection also involved investigating the impact of cannabis on other medication used for insomnia. Results: A total of 38 patients were identified as having adequate follow-up documentation to assess the impact of medical cannabis. At time of data collection, 15 patients (39%) were able to reduce or completely discontinue a prescription medication indicated for sleep. On follow-up, 27 patients (71%) reported a subjective improvement in their sleep or related condition. Only 8 patients (21%) reported any adverse effects from medical cannabis use, and these were manageable and did not require discontinuation of cannabis. Conclusion: 2022;155:xx-xx.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".