The relationship between cannabis use and diabetes: Results from the National Epidemiologic Survey on Alcohol and Related Conditions III
Bibliographic record
Abstract
INTRODUCTION AND AIMS: The relationship between cannabis use and diabetes is puzzling. Although cannabis users versus non-users should theoretically have a higher likelihood of diabetes, epidemiological studies suggest otherwise. However, previous epidemiological studies have not considered the potential confounding effects of mental health disorders. As such, the relationship between cannabis use and diabetes was examined while accounting for a range of potential confounders, including mental health disorders. DESIGN AND METHODS: Data were obtained from the National Epidemiologic Survey on Alcohol and Related Conditions III. Chi-square tests were used to compare socio-demographics, lifestyle behaviours, physical health disorders and mental health disorders between diabetics and non-diabetics. Measures that exhibited statistical significance in these tests were subsequently included in multiple logistic regression analyses to quantify the relationships between lifetime and 12-month cannabis use and diabetes. RESULTS: Although there was a considerable attenuation in the magnitude of the odds ratios after adjustment for confounders, there was still a decreased likelihood of diabetes for cannabis users versus non-users. The corresponding odds ratios of diabetes were 0.81 (95% confidence interval 0.70, 0.94) and 0.51 (95% confidence interval 0.41, 0.63) for lifetime and 12-month cannabis use, respectively. DISCUSSION AND CONCLUSIONS: A decreased likelihood of diabetes for cannabis users versus non-users was indicated after accounting for a range of potential confounders, including mental health disorders. Before the protective effects of cannabis use for diabetes can be suggested, further epidemiological studies are needed that incorporate prospective designs, as well as feature innovative exposure measurements and statistical analyses.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".