Permission to prescribe: do cardiologists need permission to prescribe diabetes medications that afford cardiovascular benefit?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Antihyperglycemic therapies including sodium glucose contransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) have been demonstrated to confer significant cardiovascular benefit and reduce future events in patients with type 2 diabetes mellitus (T2DM). However, despite positive data from cardiovascular outcome trials, these therapies remain underutilized in a large proportion of patients who have clinical indications and meet coverage guidelines for their initiation. One of the causes of the observed gap between scientific evidence and clinical cardiology practice is therapeutic hesitancy (otherwise known as therapeutic inertia). The purpose of this review is to discuss the contributors to therapeutic hesitancy in the implementation of these evidence-based therapies and, more importantly, provide pragmatic solutions to address these barriers. RECENT FINDINGS: Recent studies have demonstrated that clinicians may not initiate cardiovascular protective therapies due to a reluctance to overstep perceived interdisciplinary boundaries, concerns about causing harm due to medication side effects, and a sense of unfamiliarity with the optimal choice of therapy amidst a rapidly evolving landscape of T2DM therapies. SUMMARY: Herein, we describe a multifaceted approach aimed at creating a 'permission to prescribe' culture, developing integrated multidisciplinary models of care, enhancing trainees' experiences in cardiovascular disease prevention, and utilizing technology to motivate change. Taken together, these interventions should increase the implementation of evidence-based therapies and improve the quality of life and cardiovascular outcomes of individuals with T2DM.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".