Population Trends in All‐Cause Mortality and Cause Specific–Death With Incident Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND Limited studies have evaluated population-level temporal trends in mortality and cause of death in patients with contemporary managed atrial fibrillation. This study reports the temporal trends in 1-year overall and cause-specific mortality in patients with incident atrial fibrillation. METHODS AND RESULTS Patients with incident atrial fibrillation presenting to an emergency department or hospitalized in Ontario, Canada, were identified in population-level linked administrative databases that included data on vital statistics and cause of death. Temporal trends in 1-year all-cause and cause-specific mortality was determined for individuals identified between April 1, 2007 (fiscal year [FY] 2007) and March 31, 2016 (FY 2015). The study cohort consisted of 110 302 individuals, 69±15 years of age with a median congestive heart failure, hypertension, age (≥75 years), diabetes mellitus, stroke (2 points), vascular disease, age (≥65 years), sex category (female) score of 2.8. There was no significant decline in the adjusted 1-year all-cause mortality between the first and last years of the study period (adjusted mortality: FY 2007, 8.0%; FY 2015, 7.8%; P for trend=0.68). Noncardiovascular death accounted for 61% of all deaths; the adjusted 1-year noncardiovascular mortality rate rose from 4.5% in FY 2007 to 5.2% in FY 2015 (P for trend=0.007). In contrast, the 1-year cardiovascular mortality rate decreased from 3.5% in FY 2007 to 2.6% in FY 2015 (P for trend=0.01). CONCLUSIONS Overall 1-year all-cause mortality in individuals with incident atrial fibrillation has not improved despite a significant reduction in the rate of cardiovascular death. These findings highlight the importance of recognizing and managing concomitant noncardiovascular conditions in patients with atrial fibrillation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".