The Continuing Scourge of Atherosclerotic Cardiovascular Disease: Importance of Multidisciplinary and Innovative Person-Centred Approaches
Bibliographic record
Abstract
This article discusses the complex approach to managing atherosclerotic cardiovascular disease (ASCVD), focusing on person-centred care (PCC) to align treatment strategies with individual patient narratives, values, and preferences. It identifies significant challenges in management of ASCVD, such as the necessity for multidisciplinary strategies and the need for enhanced patient care, particularly given the coexistence of ASCVD with other cardiometabolic risk factors. The paper points out existing practice gaps, including limited patient-provider information sharing and decision making, and considers the role of technology in personalizing care and improving outcomes. Strategies such as electronic health records, telehealth platforms, and motivational interviewing are examined for their potential to boost patient engagement and adherence to treatment. In addition, the article discusses systemic issues such as health care provider burnout and the importance of creating customized care plans for patients with multiple health conditions. The integration of varied approaches, including the involvement of community pharmacists and health coaches, is suggested as important for effective management of ASCVD. This review highlights the need for an innovative, holistic strategy for management of ASCVD and advocates for a transformative shift toward PCC that integrates individual, community, and system-level interventions to enhance patient engagement, therapy adherence, and overall 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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".