Person-Centered Models for Cardiovascular Care: A Review of the Evidence: A Scientific Statement From the American Heart Association
Bibliographic record
Abstract
Cardiovascular disease remains the leading cause of death and disability in the United States and globally. Disease burden continues to escalate despite technological advances associated with improved life expectancy and quality of life. As a result, longer life is associated with multiple chronic cardiovascular conditions. Clinical guidelines provide recommendations without considering prevalent scenarios of multimorbidity and health system complexities that affect practical adoption. The diversity of personal preferences, cultures, and lifestyles that make up one's social and environmental context is often overlooked in ongoing care planning for symptom management and health behavior support, hindering adoption and compromising patient outcomes, particularly in groups at high risk. The purpose of this scientific statement was to describe the characteristics and reported outcomes in existing person-centered care delivery models for selected cardiovascular conditions. We conducted a scoping review using Ovid MEDLINE, Embase.com, Web of Science, CINAHL Complete, Cochrane Central Register of Controlled Trials through Ovid, and ClinicalTrials.gov from 2010 to 2022. A range of study designs with a defined aim to systematically evaluate care delivery models for selected cardiovascular conditions were included. Models were selected on the basis of their stated use of evidence-based guidelines, clinical decision support tools, systematic evaluation processes, and inclusion of the patient's perspective in defining the plan of care. Findings reflected variation in methodological approach, outcome measures, and care processes used across models. Evidence to support optimal care delivery models remains limited by inconsistencies in approach, variation in reimbursement, and inability of health systems to meet the needs of patients with chronic, complex cardiovascular conditions.
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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 | 0.044 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".