Peripheral artery disease (PAD) in primary care—educational experiences for PAD primary care in England—a mixed-method study
Bibliographic record
Abstract
BACKGROUND: Peripheral artery disease (PAD), the pathophysiologic narrowing of arterial blood vessels of the lower leg due to atherosclerosis, is a highly prevalent disease, with sharp increases in prevalence with age. Primary care is ideally located to identify and manage PAD. OBJECTIVES: This study aims to identify the educational experiences, opinions, and confidence of primary care clinicians (PCCs) regarding PAD. METHOD: This mixed-method study was conducted within primary care in England. An online survey was completed with follow-on semistructured interviews, between January and September 2021, with PCCs, namely GPs, practice nurses, and allied professionals (survey n = 874, interviews n = 50). RESULTS: PCCs report variation in PAD education received, where the content could not often be recalled. Patient-focussed experiential and self-directed learning, formed the largest method to gain PAD education. All PCCs recognized that they have an important role in recognizing PAD yet confidence in recognizing and diagnosing PAD was lacking. PCCs acknowledged that late or missed PAD diagnosis resulted in significant patient morbidity and mortality. Yet many did not recognize PAD as a common disease. CONCLUSION: As "specialist-generalists" with finite resources, education provided to primary care needs to be applicable for the multimorbid patient presentations often seen, utilizing resources available in primary care, with consideration to the time constraints endured.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".