National Interhospital Transfer for Patients With Acute Cardiovascular Conditions
Bibliographic record
Abstract
BACKGROUND: Treatment of ST-elevation myocardial infarction (STEMI) in Canada is protocolized, and timely patient transfer can improve outcomes. Population-based processes of care in Canada for other cardiovascular conditions remain less clear. We aimed to describe the interhospital transfer of Canadian patients with acute cardiovascular disease. METHODS: We reviewed the Canadian Institute for Health Information Discharge Abstract Database for adult patients hospitalized with acute cardiovascular disease between 2013 and 2018. We compared patient characteristics and clinical outcomes based on transfer status (transferred, nontransferred) and presenting hospital (teaching, large community, medium community, and small community hospitals). The primary outcome of interest was in-hospital mortality. RESULTS: There were 476,753 patients with primary acute cardiovascular diagnoses, 48,579 (10.2%) of whom were transferred. Transferred patients were more frequently younger, male, and had fewer comorbidities. The most common diagnoses among transferred patients were non-STEMI (44.2%), STEMI (29.0%), and congestive heart failure (9.4%). Using teaching hospitals as a reference, transfer to large and medium community hospitals was associated with lower hospital mortality (adjusted odds ratio: 0.83, 95% confidence interval: 0.75-0.91 and 0.45, 95% confidence interval: 0.39-0.52, respectively). CONCLUSIONS: Approximately 10% of patients with acute cardiovascular conditions are transferred to another hospital. Patient transfer may be associated with lower in-hospital mortality, with possible variability based on diagnosis, comorbidities, hospital of origin, and destination hospital. Further investigation into the optimization of care for patients with acute cardiovascular disease, including transfer practices, is warranted as regionalized care models continue to develop.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".