Specialized multi-disciplinary heart failure clinics in Ontario, Canada: an environmental scan
Bibliographic record
Abstract
BACKGROUND: Multi-disciplinary heart failure (HF) clinics have been shown to improve outcomes for HF patients in randomized clinical trials. However, it is unclear how widely available specialized HF clinics are in Ontario. Also, the service models of current clinics have not been described. It is therefore uncertain whether the efficacy of HF clinics in trials is generalizable to the HF clinics currently operating in the province. METHODS: As part of a comprehensive evaluation of HF clinics in Ontario, we performed an environmental scan to identify all HF clinics operating in 2010. A semi-structured interview was conducted to understand the scope of practice. The intensity and complexity of care offered were quantified through the use of a validated instrument, and clinics were categorized as high, medium or low intensity clinics. RESULTS: We identified 34 clinics with 143 HF physicians. We found substantial regional disparity in access to care across the province. The majority of HF physicians were cardiologists (81%), with 81% of the clinics physically based in hospitals, of which 26% were academic centers. There was a substantial range in the complexity of services offered, most notably in the intensity of education and medication management services offered. All the clinics focused on ambulatory care, with only one having an in-patient focus. None of the HF clinics had a home-based component to care. CONCLUSIONS: Multiple HF clinics are currently operating in Ontario with a wide spectrum of care models. Further work is necessary to understand which components lead to improved patient outcomes.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 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".