Associations with anticoagulation: a cross-sectional registry-based analysis of stroke survivors with atrial fibrillation
Bibliographic record
Abstract
OBJECTIVE: To describe vitamin K antagonist (VKA) anticoagulation prescribing patterns in stroke survivors with atrial fibrillation (AF), with particular emphasis on sociodemographic associations with VKA prescription. METHODS: We conducted a cross-sectional analysis of city-wide Glasgow primary care data held as part of the Local Enhanced Services (LES) for the year 2010. We collated clinical and sociodemographic data of community-dwelling ischaemic stroke survivors with AF, including risk factors; comorbidity; socioeconomic status and prescribing. We described stroke risk and bleeding risk using recommended stratification tools (CHA2DS2-VASC and HAS-BLED). Univariate and multivariate associations with anticoagulant prescription were described by ORs and corresponding 95% CI. RESULTS: We identified 3429 community-dwelling, ischaemic stroke survivors with AF; median age 78 (IQR 72-84); 1699 (49%) male. Median CHA2DS2-VASC score was 5 (IQR 4-6). VKA was prescribed in 1165 (34%). On univariate analysis, higher CHA2DS2-VASC was associated with fewer VKA prescriptions (OR 0.90, 95% CI 0.45 to 0.95). On multivariate analysis, older age (OR 0.97, 95% CI 0.96 to 0.98) and higher deprivation scores (OR 0.59, 95% CI 0.57 to 0.76) were independently associated with non-prescription of VKA. CONCLUSIONS: Anticoagulation was underused in this high-risk population, and those at highest risk were less likely to be treated. Strategies need to be developed to improve prescription of anticoagulation treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".