The longitudinal relationship between cannabis use and hypertension
Bibliographic record
Abstract
Abstract Introduction The relationship between cannabis use and hypertension is not clear based on prior epidemiological studies. Thus, we examined this relationship over a 3‐year follow‐up period using a large population‐based sample from the USA. Methods Self‐reported longitudinal data were obtained from the National Epidemiologic Survey on Alcohol and Related Conditions Wave 1 (2001/2002) and Wave 2 (2004/2005). The sample was restricted to participants who did not report hypertension at baseline (n = 26 844; 51% 40 years and older, 51% female, 71% white). χ2‐tests were used to examine the distributions of confounders stratified by the incidence of hypertension. Thereafter, multiple logistic regression analyses were conducted to quantify the relationships between lifetime cannabis use, 12‐month cannabis use and 12‐month cannabis use frequency and incidence of hypertension while adjusting for confounders. Results Cannabis use was associated with a decreased incidence of hypertension in the unadjusted analyses. However, the relationships were confounded by age. After adjustment for all confounders, neither lifetime cannabis use (odds ratio, 95% confidence interval 0.89, 0.77 to 1.02), 12‐month cannabis use (0.78, 0.56 to 1.09) nor 12‐month cannabis use frequency [at least monthly use (0.85, 0.57 to 1.28) and less than monthly use (0.67, 0.40 to 1.11)] were associated above chance with the incidence of hypertension. Discussion and Conclusions Lifetime cannabis use, 12‐month cannabis use and 12‐month cannabis use frequency were not associated with the incidence of hypertension.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".