A population-based study of cause-specific mortality in First Nations Australians with cirrhosis: impact of cardiometabolic comorbidities and liver disease risk factors
Bibliographic record
Abstract
Background: Liver disease is an important contributor to high mortality in First Nations Australians. We describe cause-specific mortality by First Nations status in people with cirrhosis. Methods: Population-based retrospective cohort analysis of all adults with cirrhosis admitted to hospitals in the state of Queensland (2007-2022). Patients (1909 First Nations and 20,584 non-First Nations) were followed from the first admission with cirrhosis until date of death, liver transplant, or 31 December 2022, whichever came first. Multivariable Cox regression and Fine and Gray proportional subhazard models were used to assess differences in mortality according to First Nations status. Findings: During a median follow-up of 6.9 years (IQR 3.5-11.1), 995 (52.1%) First Nations and 11,367 (55.2%) non-First Nations patients died. First Nations people died on average 9.4 years younger than non-First Nations Australians (57.0 years (SD = 12.1) vs 66.4 years (SD = 12.2), respectively). Approximately half of First Nations (48.9%) and non-First Nations (50.4%) deaths had liver disease as their underlying cause, and the 10-year liver-related mortality did not differ according to First Nations status (adjusted-sHR = 0.92, 95% CI 0.83-1.01). First Nations patients had a 1.6-fold increased risk of 10-year mortality due to cardiovascular disease (adjusted-sHR = 1.59, 95% CI 1.29-1.96), diabetes (adjusted-sHR = 1.60, 95% CI 1.07-1.52), and infections/parasitic diseases (adjusted-sHR = 1.61, 95% CI 1.12-2.23) vs non-First Nations patients. Interpretation: Mortality due to cardiovascular disease, diabetes, and infections/parasitic diseases are 60% higher in First Nations Australians with cirrhosis. The higher non-liver disease mortality in First Nations Australians reinforces the need for a holistic approach to management of metabolic comorbidities in patients with cirrhosis. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".