Relationship Between Smoking and Psychiatric and Somatic Comorbidities in Older Age Bipolar Disorder: Lien entre le tabagisme et les affections psychiatriques et somatiques concomitantes chez les personnes âgées atteintes de trouble bipolaire
Bibliographic record
Abstract
OBJECTIVE: Smoking has been associated with psychiatric and somatic comorbidities in bipolar disorder (BD) populations. However, studies in older age BD (OABD) populations are sparse. We hypothesized that among individuals with OABD, current and former smokers would have worse psychiatric and somatic comorbidities parameters compared to never smokers. METHOD: = 984). Smoking status was categorized into current smokers, former smokers, and never smokers. The distribution of demographic and clinical variables was assessed. The associations between smoking status and the clinical variables were examined using multivariable models that adjusted for age, sex, and study. Multivariable models were repeated, restricting to individuals with and without cardiovascular or respiratory (cardiorespiratory) comorbidity. RESULTS: Our study sample was 52.8% female with a mean age of 62 years and included 347 (35.3%) never smokers, 222 (22.6%) former smokers, and 415 (42.2%) current smokers. After controlling for age, sex, and study, current depression was more prevalent in former versus never smokers and current versus never smokers. Cardiovascular comorbidity was more prevalent among former than never smokers. More current versus never smokers were taking antipsychotic medications and more current versus never smokers having lifetime substance use disorders. When stratifying by the presence of cardiorespiratory comorbidity, the only statistically significant association was higher functioning in never versus current smokers in participants without cardiorespiratory comorbidity, though non-statistically significant relationships were present between lifetime smoking and depression across strata. CONCLUSIONS: The relationship of smoking with depression and substance use disorders is largely independent of age, sex, and, for the depression relationship, cardiorespiratory comorbidity. More smokers taking antipsychotic medications suggests that smoking is associated with a more severe BD course. Cardiovascular comorbidity may serve as a motivating factor for smoking cessation.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".