The Canadian Heart Failure (CAN-HF) Registry: A Canadian Multicentre, Retrospective Study of Inpatients With Heart Failure
Bibliographic record
Abstract
Background: Despite recent advances in the management of patients with heart failure (HF), national data regarding the quality of care provided are lacking. The Canadian Heart Failure (CAN-HF) Registry was designed to obtain contemporary, real-world data describing the management of patients with HF. Methods: Quality of care in patients admitted for acute HF (AHF), in relation to guidelines and national HF quality indicators, was assessed as part of the CAN-HF Registry study. Results: A total of 943 patients admitted to the hospital with AHF were included in this analysis. Patient weight was not recorded on admission for 26% of patients, with daily weight being captured in only 61% of patients. Only 54% of inpatients received left ventricular ejection fraction assessment while hospitalized. Patient education was documented in 31% of patients prior to discharge, with 51% receiving instructions to follow up with a specialist upon discharge, and 2% being referred to a cardiac rehabilitation program. Although use of guideline-directed medical therapy increased during hospitalization, the proportions of patients receiving renin-angiotensin-aldosterone inhibition (63%), beta-blockade (80%), and mineralocorticoid receptor antagonist (40%) upon discharge indicate that potential room for improvement exists. Conclusions: The CAN-HF Registry study demonstrated a potential quality-of-care gap in the management of patients admitted with AHF.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".