Exploring Understanding of Peripheral Artery Disease among Patients at High-Risk in Saudi Arabia: Results from an Interview-Based Study
Bibliographic record
Abstract
BACKGROUND: The level of awareness of peripheral artery disease (PAD) in Saudi Arabia, especially among populations at high risk, is not currently well known. Therefore, our objective was to assess the existing level of awareness among patients who are at high risk of PAD, as well as their comprehension of the disease. METHOD: An interview-based cross-sectional study included 1035 participants with risk factors for PAD and collected data on demographics and knowledge domains related to PAD. RESULTS: -tests and ANOVA. Overall, participants exhibited poor knowledge, with a mean score of 5.7 out of 26. The highest scores were observed in the risk factor and preventive measure domains, with means of 1.8 out of 7 and 1.8 out of 6, respectively. The factors associated with higher knowledge scores included older age, male gender, higher education, healthcare profession, interviews in vascular settings, previous awareness of PAD, and prior cardio-cerebrovascular interventions. CONCLUSION: This study underscores the inadequate knowledge of PAD among high-risk individuals. Targeted educational initiatives are essential to bridge this knowledge gap, potentially reducing the burden of PAD-related complications and improving patient outcomes. Efforts should focus on raising awareness about PAD, particularly among high-risk populations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".