A 2020 Environmental Scan of Heart Failure Clinics in Ontario
Bibliographic record
Abstract
Background Multidisciplinary heart failure (HF) clinics decrease hospital admission rates and healthcare use, while improving patient outcomes. To understand the contemporary availability of HF clinics in Ontario, Canada, and the services provided, we performed an environmental scan of physician-led and nurse practitioner (NP)–led HF clinics. Methods Between November, 2019 and February 2020, we identified Ontario HF clinics led by physicians or NPs. Following an invitation, we conducted a semi-structured interview to evaluate the services offered and qualitatively compared our findings to the results of the 2010 Ontario provincial survey. Results The number of HF clinics (36 vs 34 in 2010) and physicians (157 vs 143 in 2010) have not changed since the 2010 survey. Of the 36 clinics we identified, 30 participated in our interview (22 physician-led and 8 NP-led). Twenty-five clinics (83%) were hospital-based, of which 9 (30%) were part of an academic institution. Comparisons of our findings to the 2010 study on 30 clinics show an approximately 3-fold increase ( P <0.001) in both median annual and new patient visits. As previously reported, the clinics varied in services offered, but trended toward an increased availability of onsite echocardiography, exercise-stress testing, and nuclear cardiology. Conclusions Compared to the survey performed a decade ago, the number of HF clinics and physicians have not changed, and the services provided remain heterogenous. However, the increased number of patients served suggests a greater demand for these clinics. Improving the accessibility of these clinics and standardizing the service model are critical to improving 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".