Proposal for an Ambulatory Heart Failure Management Curriculum for Cardiology Residency Training Programs
Bibliographic record
Abstract
Background: The suboptimal implementation of guideline-directed medical therapy (GDMT) for heart failure (HF) patients has been linked with poor clinical outcomes. Little is known about the potential role of cardiology residency training programs in improving trainees' (ie, future cardiologists') ability to utilize GDMT. Methods: In this survey-based study, we examined the degree of exposure to ambulatory HF patient management among cardiology trainees in Canada. All cardiology residency program directors (n = 15; 100% response rate) completed our survey. Results: Although 9 programs (60%) mandated ≥ 3 ambulatory cardiology rotations, only 3 (20%) required ≥ 2 ambulatory HF rotations. When HF rotations were provided, only 7 programs (47%) offered moderate or higher exposure to ambulatory nontransplant HF patients (defined as ≥ 5 clinics/rotations). This element was independent of program- and institution-specific characteristics. All institutions had a multidisciplinary HF clinic, and the majority (13 [87%]) had access to an inpatient HF service, a consultative HF service, and/or a specialist pharmacist. Furthermore, 13 program directors (87%) agreed on the importance of adopting HF training curriculum and their program's readiness to implement such a module. Conclusions: The current state of HF training among cardiology residencies is suboptimal and in need of improvement. Most programs have access to a HF clinic, a specialist pharmacist, or an inpatient consultative service, which would facilitate adoption of a HF management curriculum that focuses on practical and experiential aspects of GDMT optimization. This program, which is under development, will be offered to training programs nationwide, to enable trainees to manage this growing and increasingly complex patient population.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".