Do Outcomes for Patients With Heart Failure Vary by Emergency Department Volume?
Bibliographic record
Abstract
BACKGROUND: Heart failure is a common Emergency Department (ED) presentation but whether ED volume influences patient outcomes is unknown. METHODS AND RESULTS: Retrospective cohort of all adults presenting to 93 EDs between 1999 and 2009 with a most responsible diagnosis of heart failure (n=44 925 ED visits; mean age, 76.4 years). Cases seen in low-volume EDs had less comorbidities and were less likely to be hospitalized (54.5%) than those seen in medium (61.8%; adjusted odds ratio [aOR] 1.16, [95% confidence interval {CI} 1.10-1.23]) or high-volume EDs (73.6%; aOR, 1.95 [95% CI, 1.83-2.07]). Of patients treated and released, low-volume ED cases exhibited higher risk of death/hospitalization/ED visit in the subsequent 7 (22.0%) and 30 days (44.9%) than medium (16.3%; aOR, 0.81 [95% CI, 0.73-0.90], and 35.3%; aOR, 0.79 [95% CI, 0.73-0.86]) or high-volume ED cases (13.0%; aOR, 0.69 [95% CI, 0.61-0.78], and 30.2%; aOR, 0.67 [95% CI, 0.61-0.74]). Of patients hospitalized at the time of their index ED visit, low-volume ED cases exhibited a higher risk of 30-day death/all-cause readmission (24.3%) than those seen in medium (21.9%; aOR, 0.83 [95% CI, 0.76-0.91]) or high-volume EDs (18.1%; aOR, 0.77 [95% CI, 0.70-0.85]). CONCLUSIONS: Low-volume EDs were more likely to discharge patients with heart failure home, but low-volume ED cases exhibited worse outcomes (driven largely by readmissions or repeat ED visits). Interventions to improve management of acute heart failure are required at low-volume sites.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".