Impact of Cannabis Smoking on Multiple Sleep Latency Test Outcomes
Bibliographic record
Abstract
Our purpose was to evaluate how cannabis smoking influenced multiple sleep latency test (MSLT) outcomes. This was a retrospective study of all adults that had undergone a MSLT at St. Michael's Hospital (Toronto, Ontario, Canada) from 1 January 2008 until 31 December 2018. Three groups of persons were considered: active cannabis-only smokers, active tobacco-only smokers and non-active cannabis and tobacco smokers. A range of outcomes from the MSLT and preceding overnight polysomnogram were evaluated. Descriptive statistics at the univariate level were used. We identified a total of 139 individuals undergoing MSLT, of whom 9 (6.5%) were active cannabis-only smokers, 14 (10.0%) were active tobacco-only smokers and 116 (83.4%) were non-smokers. There were non-significant trends among cannabis-only smokers versus non-smokers and tobacco-only smokers towards lower mean sleep onset latency on MSLT (8.1 min vs. 9.2 min and 10.5 min, respectively) and there was a greater proportion of severe sleepiness (33.3% vs. 22.4% and 14.3%, respectively), having at least one REM sleep onset period (55.6% vs. 28.4% and 42.9%, respectively), narcolepsy diagnosis (22.2% vs. 8.6% and 7.1%, respectively), and idiopathic hypersomnia diagnosis (33.3% vs. 30.2% and 14.3%). Although we found no significant differences among the groups we evaluated, there were non-significant trends in multiple outcomes indicative of hypersomnia among active cannabis-only smokers, most notable of which were more frequent (and potentially incorrect) diagnoses of narcolepsy and idiopathic hypersomnia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".