Clinical Heart fAilure Management Program: Changing the practice by partnering primary care and specialists (CHAMP-HF)
Bibliographic record
Abstract
Background: While significant gains were made in the management of heart failure (HF), most patients are still diagnosed when they are acutely ill in hospital, often with advanced disease. Earlier diagnosis in the community could lead to improved outcomes. Whether a partnership and an educational program for primary care providers (PCP) increase HF awareness and management is unknown. Methods: We conducted an observational study between March 2019 and June 2020 during which HF specialists gave monthly HF conferences to PCP. Using a pre-post design, medical charts and administrative databases were reviewed and a questionnaire was completed by participating PCP. Primary and secondary endpoints included: 1) the number of patients diagnosed with HF, 2) implementation of GDMT for patients with HFrEF; 3) PCPs' experience and confidence. Results: Six PCP agreed to participate. Amongst the 11,909 patients of the clinic, 70 (0.59 %) patients met the criteria for HF. This number increased by 28.6 % (n = 90) after intervention. Increased use of GDMT for HFrEF patients at baseline (n = 35) was observed for all class of agents, with doubling of patients on triple therapies, from 8 (22.9 %) to 16 (45.7 %), p = 0.0047. Self-confidence on HF management was low (1, 16.7 %) but increased after the educational intervention of physicians (3, 50 %). Conclusion: An educational and collaborative approach between HF specialists and community PCP increased the number of new HF cases diagnosed, enhanced implementation of GDMT in patients with HFrEF and increase PCPs' confidence in treating HF, despite being conducted during the COVID-19 pandemic.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".