Assessing the longitudinal associations and stability of smoking and depression syndrome over a 4‐year period in a community sample with type 2 diabetes 在一个社区2型糖尿病样本中进行的为期4年的吸烟与抑郁综合征之间的纵向相关性以及稳定性的评估
Bibliographic record
Abstract
BACKGROUND: The aim of the present study was to investigate the stability and longitudinal association between depression and smoking status within a community sample with type 2 diabetes (T2D) while controlling for sociodemographic and disease-related variables. METHODS: Adults with T2D were recruited and agreed to be followed-up via random digit dialing for the Montreal Diabetes Health Study. At baseline, 1614 individuals were classified as never (n = 592), former (n = 690), light (≤10 cigarettes a day; n = 128) and moderate-heavy (11+ cigarettes a day; n = 204) smokers. Depression was assessed using the Patient Health Questionnaire-9 and individuals were classified as either "none" or having depression syndrome. Generalized estimating equations were used to test the association between depression syndrome and current smoking status while controlling for other demographic and health-related variables. RESULTS: Prevalence rates of smoking and depression showed mild to substantial agreement over time. Depression syndrome was significantly associated with moderate-heavy smoking in the fully adjusted model using cross-sectional (all four waves; odds ratio [OR] 1.46; 95% confidence interval [CI] 1.08-1.99; P < 0.05) and longitudinal (controlling for depression at baseline; OR 1.54; 95% CI 1.02-2.31; P < 0.05) data. CONCLUSIONS: Smoking and depression prevalence rates appear to be stable over time in our community sample with T2D. Moderate-heavy smoking is strongly associated with elevated depression, both in cross-sectional and longitudinal models. Persistent moderate-heavy smokers may be at increased risk of both physical and mental health complications. This burden is even greater for those with T2D.
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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 | 0.001 |
| 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) | 0.000 | 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".