Sex-specific temporal trends in ambulatory heart failure incidence, mortality and hospitalisation in Ontario, Canada from 1994 to 2013: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: To examine the temporal trends in mortality and heart failure (HF) hospitalisation in ambulatory patients following a new diagnosis of HF. DESIGN: Retrospective cohort study SETTING: Outpatient PARTICIPANTS: Ontario residents who were diagnosed with HF in an outpatient setting between 1994 and 2013. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was all-cause mortality within 1 year of diagnosis and the secondary outcome was HF hospitalisation within 1 year. Risks of mortality and hospitalisation were calculated using the Kaplan-Meier method and the relative hazard of death was assessed using multivariable Cox proportional hazard models. RESULTS: A total of 352 329 patients were studied (50% female). During the study period, there was a greater decline in age standardised 1-year mortality rates (AMR) in men (33%) than in women (19%). Specifically, female AMR at 1 year was 10.4% (95% CI 9.1% to 12.0%) in 1994 and 8.5% (95% CI 7.5% to 9.5%) in 2013, and male AMR at 1 year was 12.3% (95% CI 11.1% to 13.7%) in 1994 and 8.3% (95% CI 7.5% to 9.1%) in 2013. Conversely, age standardised HF hospitalisation rates declined in men (11.4% (95% CI 10.1% to 12.9%) in 1994 and 9.1% (95% CI 8.2% to 10.1%) in 2013) but remained unchanged in women (9.7% (95% CI 8.3% to 11.3%) in 1994 and 9.8% (95% CI 8.6% to 11.0%) in 2013). CONCLUSION: Among patients with HF over a 20-year period, there was a greater improvement in the prognosis of men compared with women. Further research should focus on the determinants of this disparity and ways to reduce this gap in outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".