Vitamin K Antagonists and Cognitive Impairment: Results From a Cross-Sectional Pilot Study Among Geriatric Patients
Bibliographic record
Abstract
BACKGROUND: Vitamin K is involved in brain physiology, suggesting that its deficiency induces cognitive decline. Our objective was to determine whether using vitamin K antagonists (VKAs) was associated with cognitive impairment among geriatric patients. METHODS: Two hundred sixty-seven older patients (mean, 83.4 ± 8.1 years; 56.9% female) were categorized according to cognitive impairment (ie, Mini-Mental State Examination ≤ 25). The regular use of VKAs was sought by questioning the patients, relatives, and family physicians. Age, gender, body mass index, comorbidity burden, mood and executive functioning, history of atrial fibrillation, ischemic stroke, intracranial hemorrhage and transient ischemic attack, use of other anticoagulants and antiplatelet medications, and severe renal failure were used as potential confounders. RESULTS: Compared with participants without cognitive impairment (n = 70), those with Mini-Mental State Examination ≤ 25 used more frequently VKAs (p = .038). The risk of cognitive impairment was 15% higher with VKAs, specifically with fluindione. Using VKAs was independently associated with cognitive impairment (fully adjusted odds ratio = 17.4 [95% CI: 1.4-224.2], p = .028). CONCLUSIONS: We found more frequent cognitive impairment associated with the use of VKAs, specifically fluindione, among geriatric patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".