Serotonergic antidepressant use and morbidity and mortality among older adults with COPD
Bibliographic record
Abstract
We evaluated the relationship between new selective serotonin reuptake inhibitor (SSRI) or serotonin–noradrenaline reuptake inhibitor (SNRI) drug use and respiratory-related morbidity and mortality among older adults with chronic obstructive pulmonary disease (COPD). This was a retrospective population-based cohort study using heath administrative data from Ontario, Canada. Individuals aged ≥66 years, with validated, physician-diagnosed COPD (n=131 718) were included. New SSRI/SNRI users were propensity score matched 1:1 to controls on 40 relevant covariates to minimise potential confounding. Among propensity score matched community-dwelling individuals, new SSRI/SNRI users compared to non-users had significantly higher rates of hospitalisation for COPD or pneumonia (hazard ratio (HR) 1.15, 95% CI 1.05–1.25), emergency room visits for COPD or pneumonia (HR 1.13, 95% CI 1.03–1.24), COPD or pneumonia-related mortality (HR 1.26, 95% CI 1.03–1.55) and all-cause mortality (HR 1.20, 95% CI 1.11–1.29). In addition, respiratory-specific and all-cause mortality rates were higher among long-term care home residents newly starting SSRI/SNRI drugsversuscontrols. New use of serotonergic antidepressants was associated with small, but significant, increases in rates of respiratory-related morbidity and mortality among older adults with COPD. Further research is needed to clarify if the observed associations are causal or instead reflect unresolved confounding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".