Cognitive function in patients with peripheral artery disease: a prospective single-Center cohort study
Bibliographic record
Abstract
AIM: The aim of the present study was to examine the association between cardiovascular comorbidities and risk factors, and cognitive function in peripheral artery disease (PAD) patients, as well as to determine the influence of cognitive function on cardiovascular outcome in a two-year follow-up. METHODS: The cognitive function of 104 PAD patients was assessed using the mini-mental test (MMSE). Ankle Brachial Index (ABI), Fontaine stage, PAD localization, cardiovascular risk factors and comorbidities were taken from the electronic patient charts. A multiple logistic regression model, which included myocardial infarction (MI), stroke/transient ischemic attack (TIA), diabetes mellitus (DM), coronary heart disease (CHD) and smoking was performed to compare patients with and without cognitive impairment. All study participants were followed for two years in order to evaluate their cardiovascular outcome, mortality and revascularisation rate. RESULTS: There was no significant difference in mini-mental state between asymptomatic and symptomatic PAD patients. ABI and PAD localization was not related to cognitive function. However, pre-existing stroke, TIA, coronary artery disease (CAD) or DM were associated with a lower MMSE score. When MMSE was dichotomized in ≤27 and >27 points, the presence of CAD, history of cerebrovascular events and DM was associated with a MMSE ≤27 in multivariate analysis. There was no association between MMSE and cardiovascular event rate. CONCLUSION: PAD patients with CAD, stroke, TIA or DM have worse cognitive function than those without these factors. There was no evidence that cognitve function influenced cardiovascular outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".