Suboptimal use of cardiovascular risk modification therapies among patients undergoing vascular surgery admitted with chronic limb-threatening ischemia
Bibliographic record
Abstract
Background Patients with peripheral arterial disease (PAD) are at an increased risk of coronary artery disease and related complications. PAD and coronary artery disease share modifiable risk factors, and pharmacological treatment reduces cardiovascular (CV) events and mortality. Characterizing prescribing trends of evidence-based CV risk-modifying medications and recognizing care gaps are important steps in improving patient outcomes. The study objective was to determine the proportion of patients with PAD-related chronic limb-threatening ischemia prescribed CV risk-modifying medications (angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker [ACEi/ARB], statin, and antiplatelet) on discharge from vascular surgery care. Methods This single-center, retrospective cohort study included patients with chronic limb-threatening ischemia admitted to the vascular surgery service at a tertiary care center. Inferential statistics were used to describe patients not prescribed CV risk-modifying medications. Multivariable logistic regression was used to determine any independent association of medication, disease, and demographic factors with a prescription for CV risk-modifying medications. Results : A total of 178 patients met the inclusion criteria, of whom 56 (32%) were prescribed an ACEi/ARB, statin, and antiplatelet medication on admission and 76 (43%) at discharge. Coronary artery disease (adjusted odds ratio [aOR]: 2.23, 95% confidence interval [CI]: 1.09-4.55) and dyslipidemia (aOR: 3.84, 95% CI: 1.87-7.88) were associated with increased odds of being prescribed CV risk-modifying medications; atrial fibrillation was associated with decreased odds (aOR: 0.19, 95% CI: 0.06-0.61). Conclusions Only 43% of the study population was prescribed an ACEi/ARB, statin, and antiplatelet medication at discharge, demonstrating a gap in care. The low prescribing rate of CV risk-modifying medications in this population warrants further investigation and highlights a key area to focus medical risk modification efforts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".