Predictors of Peripheral Artery Disease among Elderly Respondents in an Urban Hospital, Edo State Nigeria
Bibliographic record
Abstract
Peripheral artery disease (PAD) potentially affects health-related Quality of Life, Disability Adjusted Life Years (DALYs) and is a strong prognostic marker for future cardiovascular events in elderly population. PAD commonly affects the elderly but may go undiagnosed in them, probably due to the presence of other morbidities like osteoarthritis and associated muscle spasm. Aim. The aim of this study was to determine the predictors of PAD in elderly by evaluating the socio-demographic and clinical characteristics in elderly patients. Methods. A cross sectional hospital based study was carried out among 370 patients aged 60 years and above attending a Tertiary Hospital from September to November 2017. A systematic random sampling technique was utilized. A structured questionnaire was administered to collect data on socio-demographic characteristics, lifestyle variables, and medical history. The Ankle Brachial Index (ABI) was used to assess for PAD. Analysis was done using Chi-square test and logistic regression. Results. The mean age was 69.3±7 years comprising of 76.5% females, 50% of the respondents were married while 47% were widowed. After adjusting for other variables, the result of the multi-logistic regression indicated that only patients with abnormal pedal pulse were more likely to present with PAD than those with normal pedal pulse (OR=10.634, 95% CI=2.4-47.121, p=0.002). Conclusion. The study reveals that abnormal pedal pulses were significant predictors of PAD, therefore it is recommended that regular screening (clinical foot examination and ABI) should be done for elderly to achieve early detection of PAD and facilitate prompt treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".