Hypertension Management in Peripheral Artery Disease: A Mini Review
Bibliographic record
Abstract
Lower extremity peripheral artery disease (PAD) affects over 230 million adults globally, with hypertension being one of the major risk factors for the development of PAD. Despite the high prevalence, patients with hypertension who have concomitant PAD are less likely to receive adequate therapy. Through this review, we present the current evidence underlying hypertension management in PAD, guideline-directed therapies, and areas pending further investigation. Multiple studies have shown that both high and relatively lower blood pressure levels are associated with worse health outcomes, including increased morbidity and mortality. Hence, guideline-directed recommendation involves cautious management of hypertensive patients with PAD while ensuring hypotension does not occur. Although any antihypertensive medication can be used to treat these patients, the 2017 American Heart Association/American College of Cardiology (AHA/ACC), 2017 European Society of Cardiology (ESC), and 2022 Canadian guidelines favor the use of angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB) as the initial choice. Importantly, data on blood pressure targets and treatment of hypertension in PAD are limited and largely stem from sub-group studies and post-hoc analysis. Large randomized trials in patients with PAD are required in the future to delineate hypertension management in this complex patient population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".