Poor knowledge of peripheral arterial disease among the Saudi population: A cross-sectional study
Bibliographic record
Abstract
Peripheral arterial disease is a marker of severe atherosclerosis with a significantly higher risk of cardiovascular morbidity and mortality. It is often underdiagnosed and undertreated. Public and patients' perception of peripheral arterial disease is influenced by their knowledge of the condition. In this study, we aimed to evaluate the Saudi public's knowledge of peripheral arterial disease and its specific characteristics. We conducted an interview-based cross-sectional survey, and collected data on basic demographics, self-reported peripheral arterial disease awareness, and knowledge of clinical features, risk factors, preventative measures, management strategies, and potential complications of peripheral arterial disease. A total of 866 participants completed the survey (response rate, 94%); two-thirds were females. Only 295 (34%) of the surveyed participants indicated awareness of peripheral arterial disease. Overall peripheral arterial disease knowledge was low among the "peripheral arterial disease aware" group, particularly in the clinical features domain. Age > 40 years, female gender, and higher education were predictors of self-reported awareness of peripheral arterial disease. In conclusion, the Saudi public is largely unaware of peripheral arterial disease. Educational programs are important to address this critical knowledge gap.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".