Prevalence of the frailty among Saudi patients referred for myocardial perfusion imaging: A cross sectional study
Bibliographic record
Abstract
Clinical Research. Oral Presentation. Frailty is a state of vulnerability and decreased physiological response to stressors. Saudi vision 2030 stated a goal to increase Saudi life expectancy by 5 years. As the population ages, the prevalence of frailty is expected to increase. Thus, identifying tools and resources that efficiently reduces frailty among elderly patients is important. We aim to describe the prevalence and predictors of frailty among Saudi patients referred for stress testing with nuclear imaging. We included 876 patients (mean age 60.28 ± 11, females 48.29%) who underwent clinically indicated nuclear myocardial perfusion imaging using PET between January and October 2016. The Canadian Study of Health and Ageing Clinical Frailty Scale was used to assess frailty. Patients were considered frail if they had a score of four or higher. Multivariate adjusted logistic regression model was used to determine the independent predictors of elderly frail patients. In this cohort, the median age of the included patients was 61 years and the prevalence of the frailty was 40%. The frail patients were older, more frequently females and had a higher body mass index. Additionally, frailty was associated with higher prevalence of cardiovascular risk factors: hypertension, diabetes and dyslipidaemia (85%, 75%, and 46%, respectively). In fully adjusted logistic regression model, female gender, high body mass index (⩾30 kg/m2), hypertension and reduced coronary flow reserve were independent predictors of elderly frail patients. With the aging of the Saudi population, frailty prevalence is expected to increase. Elderly obese hypertensive females have the highest risk of developing frailty. Interventions to reduce frailty should be focused on this high-risk population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".