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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".