Trends in the incidence and outcomes of heart failure in Ontario, Canada: 1997 to 2007
Bibliographic record
Abstract
BACKGROUND: Heart failure is a leading cause of admission to hospital, but whether the incidence of heart failure is increasing or decreasing is uncertain. We examined temporal trends in the incidence and outcomes of heart failure in Ontario, Canada. METHODS: Using population-based administrative databases of hospital discharge abstracts and physician health insurance claims, we identified 419 551 incident cases of heart failure in Ontario between Apr. 1, 1997, and Mar. 31, 2008. All patients were classified as either inpatients or outpatients based on the patient's location at the time of the initial diagnosis. We tracked subsequent outcomes through linked administrative databases. RESULTS: The age- and sex-standardized incidence of heart failure decreased 32.7% from 454.7 per 100 000 people in 1997 to 306.1 per 100 000 people in 2007 (p < 0.001). A comparable decrease in incidence occurred in both inpatient and outpatient settings. The greatest relative decrease occurred in patients aged 85 and over. Over the study period, 1-year risk-adjusted mortality decreased from 17.7% in 1997 to 16.2% in 2007 (p = 0.02) for outpatients, with a nonsignificant decrease from 35.7% in 1997 to 33.8% in 2007 (p = 0.1) for inpatients. INTERPRETATION: The incidence of heart failure decreased substantially during the study period. Nevertheless, the prognosis for patients with heart failure remains poor and is associated with high mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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".