Associations between lung function and future cardiovascular morbidity and overall mortality in a predominantly First Nations population: a cohort study.
Bibliographic record
Abstract
Background Spirometric lung function impairment is an independent predictor of respiratory and cardiovascular disease, and mortality across a broad range of socioeconomic backgrounds and environmental settings. No contemporary studies have explored these relationships in a predominantly regional/remote First Nations population, whose health outcomes are worse than for non-First Nations populations, and First Nations people living in urban centres. Methods This was a retrospective cohort study of 1,734 adults (1,113 First Nations) referred to specialist respiratory outreach clinics in the state of Queensland, Australia from February 2012 to March 2020. Regression modelling was used to test associations between lung function and mortality and cardiovascular disease. Findings At the time of analysis (August 2020), 189 patients had died: 88 (47%) from respiratory causes and 38 (20%) from cardiovascular causes. When compared to patients with forced expiratory volume in one second (FEV 1 ) and forced vital capacity (FVC) Z-scores of >0 to -1, patients with Z-scores <-1 were at elevated mortality risk (HR=3•2, 95%CI 1•4-7•4; HR=2•6, 95%CI 1•3-5•1), and elevated cardiovascular disease risk (OR=1•5, 95%CI 1•1-2•2; OR=1•6, 95%CI 1•2-2•3). FEV 1 /FVC% Z-scores <-1 were associated with increased overall mortality (HR=1•6, 95%CI 1•1-2•3), but not cardiovascular disease (OR=1•1, 95%CI 0•8-1•4). These associations were not affected by First Nations status. Interpretation Reduced lung function even within the clinically normal range is associated with increased mortality, and cardiovascular disease in First Nations Australians. These findings highlight the importance of lung function optimisation and inform the need for future investment to improve outcomes in First Nations populations. Funding None.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".