Ankle–Brachial Index: An Ubiquitous Marker of Cognitive Impairment—The EPIDEMCA Study
Bibliographic record
Abstract
Epidemiological research on the implication of atherosclerosis in the development of cognitive impairment is lacking in low- and middle-income countries, where two-thirds of the individuals affected by dementia live. Individuals aged ≥65 years living in urban and rural areas of 2 countries in Central Africa were invited. Demographic, clinical, and biological data were collected, and the ankle-brachial index (ABI) was measured. Cognitive impairment was defined according to the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) criteria. Among 1662 participants (age 72.9 years, 59.3% females), the prevalence of cognitive impairment was 13.6%, which is higher in individuals with ABI ≤ 0.90 and ABI ≥ 1.40 than those with 0.90 < ABI < 1.40 (20.1% and 17% vs 12%, P = .0024). Cognitive impairment was significantly associated with the factors such as age (odds ratio [OR]: 1.09; 95% confidence interval [CI]: 1.07-1.12, P < .0001), female gender (OR: 2.36, 95% CI: 1.59-3.49, P < .0001), smoking (OR: 1.54, 95% CI: 1.06-2.23, P = .0026), and low ABI (≤0.90; OR: 1.52, 95% CI: 1.03-2.25, P = .0359). The ABI, a ubiquitous marker of atherosclerosis, provides independent and incremental information on susceptibility to present with cognitive disorders.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".