Effect of early physician follow-up on mortality and subsequent hospital admissions after emergency care for heart failure: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The 1-year mortality rate in patients with heart failure who are discharged from an emergency department is 20%. We sought to determine whether early follow-up after discharge from the emergency department was associated with decreased mortality or subsequent admission to hospital. METHODS: This retrospective cohort study conducted in Ontario, Canada, included adult patients who were discharged from 1 of 163 emergency departments between April 2007 and March 2014 with a primary diagnosis of heart failure. Using a propensity score-matched landmark analysis, we assessed follow-up in relation to mortality and admissions to hospital for cardiovascular conditions. RESULTS: Of 34 519 patients, 16 274 (47.1%) obtained follow-up care within 7 days and 28 846 (83.6%) within 30 days. Compared with follow-up between day 8 and 30, patients with follow-up care within 7 days had a lower rate of mortality over 1 year (hazard ratio [HR] 0.92; 95% confidence interval [CI] 0.87-0.97), and a reduced rate of admission to hospital over 90 days (HR 0.87, 95% CI 0.80-0.94) and 1 year (HR 0.92; 95% CI 0.87-0.97); the mortality rate over 90 days in this group trended to a lower rate (HR 0.90, 95% CI 0.10-1.00). Follow-up care within 30 days, compared with patients without 30-day follow-up, was associated with a reduction in 1-year mortality (HR 0.89, 95% CI 0.82-0.97) but not admission to hospital (HR 1.02, 95% CI 0.94-1.10). In this group, there was a trend toward an increase in 90-day admission to hospital (HR 1.14, 95% CI 1.00-1.29). INTERPRETATION: Follow-up care within 7 days of discharge from the emergency department was associated with lower rates of long-term mortality, as well as subsequent hospital admissions, and a trend to lower short-term mortality rates. Timely access to longitudinal care for patients with heart failure who are discharged from the emergency setting should be prioritized.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".