Stroke prevention in hospitalized patients with atrial fibrillation: a population-based study.
Bibliographic record
Abstract
BACKGROUND: Oral anticoagulants reduce the incidence of stroke by 68%, yet suboptimal use has been documented in surveys of patients with atrial fibrillation. The present study examined current patterns of anticoagulant use for patients hospitalized with atrial fibrillation across an entire health care system. METHODS: Improving Cardiovascular Outcomes in Nova Scotia (ICONS) is a prospective cohort study involving all patients hospitalized in Nova Scotia with atrial fibrillation, among other conditions. Consecutive inpatients with atrial fibrillation from October 15, 1997 to October 14, 1998 were studied. Detailed demographic and clinical data were collected and the proportion of patients using antithrombotic therapy was tabulated by risk category. Multivariate logistic regression was used to assess the relationship of various demographic and clinical factors with the use of antithrombotic agents. RESULTS: There were 2202 patients hospitalized with atrial fibrillation; 644 admitted specifically for this condition. Only 21% of patients admitted with atrial fibrillation were on warfarin sodium at admission and this increased by time of discharge. Diabetes was negatively correlated with warfarin sodium use. Histories of prosthetic valve replacement, stroke/transient ischemic attack, and heart failure were positively associated with anticoagulant use on admission. Patients with prosthetic valve replacement, heart failure, or hyperlipidemia were most likely to receive anticoagulants at discharge. CONCLUSION: Antithrombotic agents remain underused by patients with atrial fibrillation. While higher risk patients are generally targeted, this is not invariably the case; thus, diabetics remain under treated. Further work is needed to explain such anomalous practice and promote optimal antithrombotic therapy use.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".