0975 Cannabis Use and Sleep Architecture in Depression: Preliminary Findings
Bibliographic record
Abstract
Sleep abnormalities are highly prevalent among individuals with depression and these abnormalities are thought to contribute to the onset and maintenance of mood disorders. There are some indications that some people with sleep or mood problems self-medicate with cannabis in attempt to improve their sleep. While further work is required in this area, research in healthy adults does suggest that cannabis reduces sleep latency and wake after sleep onset. However, little is known about the impacts of cannabis use on sleep in people with depression. This study evaluated sleep architecture in depressed individuals who did or did not consume cannabis. As part of a larger retrospective study, polysomnography recordings were collated for 26 individuals with a documented history of depression and current depressive symptoms (Beck Depression Inventory-II ≥ 14) from the sleep clinic of a mental health care facility. Of these, 13 individuals consumed cannabis on the day of the sleep recording. (39.7 ± 14.8 years old, 24% male), and 13 did not (40.1 ± 14.2 years old, 24% male). A Mann-Whitney U test indicated that the percentage of stage 1 sleep was significantly greater in individuals who did not consume cannabis (Median = 21.0, Mean = 28.5, SD = 20.5) prior to sleep as compared to those who did consume (Median = 12.2, Mean = 16.8, SD = 11.09; U = 44.0, p = .038). There was no significant group difference for any other macroarchitecture sleep variable. These preliminary results suggests that, in people with depression and sleep complaints, cannabis use does not seem to be accompanied by major changes in sleep macroarchitecture, but may be linked to a reduced proportion of shallow (stage 1) sleep. While this would need to be replicated in larger samples, future randomized placebo-controlled trials should assess whether cannabis may actively interact with sleep disturbances linked to depression. N/A.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".