Cannabis use, sleep and mood disturbances among persons with epilepsy – A clinical and polysomnography study from a Canadian tertiary care epilepsy center
Bibliographic record
Abstract
OBJECTIVE: Interest in anti-seizure properties of cannabinoids is increasing, with the rise in prevalence of recreational and medical cannabis use, especially across Canada. In a recent study on people with epilepsy (PWE), cannabis use showed a strong association with poor psychosocial health. Sleep and mood comorbidities are highly prevalent in epilepsy, and are common motivations for cannabis use. The primary objective of this study was to assess demographic, subjective and objectively assessed sleep quality and mood related differences among PWE who regularly use cannabis compared to those who do not. METHODS: Consecutive consenting patients with a confirmed epilepsy diagnosis, admitted to our Epilepsy Monitoring Unit, over a 3-year period (2019-2022) were enrolled. Detailed epilepsy-related data and self-reported sleep [Pittsburgh Sleep quality index (PSQI)], Epworth Sleepiness Scale (ESS)], mood [(Beck's Depression Inventory (BDI) and Beck's Anxiety inventory (BAI)] and cannabis use related data were collected. Overnight polysomnography (PSG) was conducted on the first night of admission, with simultaneous 18-channel video-EEG. Sleep (PSG) scoring followed American Academy of Sleep Medicine guidelines by a scorer blinded to clinical details. RESULTS: Among 51 patients with similar seizure control, 25 (13 F) reported cannabis use (mean age 36.3+14.8 years) and were significantly younger than 26 (18 F) non-users (mean age 48.3+15 years). Cannabis users had significantly better subjective sleep quality (mean PSQI scores 7.2+2.9 vs 10.2+5.2 respectively). Most patients endorsed sleepiness (Cannabis users with ESS scores greater than 10; 91.3 %, 77.3 % in non-users) and moderate to extreme depression (BDI) scores. No significant differences were observed in objective sleep parameters. BDI score significantly predicted PSQI and ESS scores on multiple logistic regression analysis. SIGNIFICANCE: Despite a significant age difference, self-reported sleep quality is better among PWE who report regular cannabis use compared to non-users. However, there is no significant difference in objective sleep quantity and quality from PSG between the two groups. Additionally, severity of depressive symptoms is a significant predictor of sleep quality and of excessive daytime sleepiness among PWE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".