Cardiovascular morbidity and the use of inhaled bronchodilators
Bibliographic record
Abstract
We used the Manitoba Health database to examine the relationship between use of inhaled respiratory drugs in people with chronic obstructive respiratory diseases and cardiovascular hospitalizations from 1996 through 2000. The drugs examined were beta agonists [BA], ipratropium bromide IB, and inhaled steroids (ICS). End points were first hospitalizations for supraventricular tachycardia, myocardial infarction, heart failure or stroke. A nested case control analysis was employed comparing people with and without cardiovascular events. Cases and controls were matched for gender and age, and conditional logistic regression was used in multivariate analysis considering other respiratory drugs, respiratory diagnosis and visit frequency, non-respiratory, non-cardiac comorbidities, and receipt of drugs for cardiovascular disease. In univariate analyses, BA, IB and ICS were all associated with hospitalizations for cardiovascular disease, but in multivariate analyses ICS did not increase risk while both BA and IB did. There were interactions between respiratory and cardiac drugs receipt in that bronchodilator associated risks were higher in people not taking cardiac drugs; this was especially true for stroke. There were strong interactions with specific cardiac drugs; for example, both BA and IB substantially increased the risk of supraventricular tachycardia in patients not anti-arryhthmic agents, but not in the presence of such agents. We conclude that bronchodilator therapy for chronic obstructive diseases is associated with increased cardiovascular risk, especially in patients without previous cardiovascular diagnoses, and that this is unlikely due to the severity of the respiratory disease, since risk was not increased with ICS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".