Care and Outcomes of Patients Newly Hospitalized for Heart Failure in the Community Treated by Cardiologists Compared With Other Specialists
Bibliographic record
Abstract
BACKGROUND: It is not known whether subspecialty care by cardiologists improves outcomes in heart failure patients from the community over care by other physicians. METHODS AND RESULTS: Using administrative data, we monitored 38 702 consecutive patients with first-time hospitalization for heart failure in Ontario, Canada, between April 1994 and March 1996 and examined differences in processes of care and clinical outcomes between patients attended by physicians of different disciplines. We found that patients attended by cardiologists had lower 1-year risk-adjusted mortality than those attended by general internists, family practitioners, and other physicians (28.5% versus 31.7%, 34.9%, and 35.9%, respectively; all pairwise comparisons, P<0.001). The 1-year risk-adjusted composite outcome of death and readmission for heart failure was also lower for the cardiologists compared with family practitioners and other physicians but not general internists (54.7% versus 58.1%, 58.3%, and 55.4%; P<0.001, P<0.001, and P=0.39, respectively). Multivariable hierarchical modeling demonstrated a significant physician-level effect for both outcomes in favor of the cardiologists, particularly against non-general internists. Cardiologist care was associated with higher adjusted rates of invasive interventions and postdischarge prescriptions of heart failure medications. CONCLUSIONS: In this population-based cohort, heart failure patients attended by cardiologists in hospital had lower risk of death as well as the composite risk of death or readmission than patients attended by noncardiologists. These data raise the need to identify specialty-driven differences in processes of care for heart failure patients, which may explain the observed disparity in clinical outcomes that presently favor cardiologist care.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".