Lower Extremity Artery Disease and Cognitive Impairment
Bibliographic record
Abstract
Abstract Background The association between cerebrovascular disease and cognitive impairment is well known, but the impact of lower extremity arterial disease (LEAD) on neuropsychological performance is less established. Objectives The aim of this study was to investigate the influence of LEAD on cognitive impairment. Materials and Methods A total of 20 patients with LEAD, classified by Fountain’s stage IIB, qualified for revascularization surgery has been included in this prospective study. Neuropsychological assessments have been done using MoCA and CANTAB test. Fifteen patients qualified for hernia surgery, without peripheral artery disease served as a control group. Linear regression model has been applied to assess the connection between LEAD and cognitive impairment. Results Differences between the study groups reach significance in both MoCA and CANTAB test. In MoCA test, patients with LEAD had lower levels of performance in attention (p = 0.0254), visuospatial/executive (p = 0.0343) and delayed recall (p = 0.0032). The mean MoCA score was below 26 points. In CANTAB test, patients with LEAD performed worse in visual memory and learning. After adjusted for common cerebrovascular risk factors, LEAD was significantly correlated with cognitive impairment defined as MoCA score < 26 points. Conclusions Lower extremity artery disease is associated with cognitive impairment independently of cerebrovascular risk factors.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".