Pre‐ and post‐stroke oral antithrombotics and mortality in patients with ischaemic stroke
Bibliographic record
Abstract
BACKGROUND: Reducing stroke occurrence requires the effective management of cardiovascular and other stroke risk factors. PURPOSE: To describe pre- and post-stroke medication use, focusing on antithrombotic therapy and mortality risk, in individuals hospitalised for ischaemic stroke (IS) in the United Kingdom. METHOD: Using primary care electronic health records from the United Kingdom, we identified patients hospitalised for IS (July 2016-September 2019) and classed them into three groups: atrial fibrillation (AF) diagnosed pre-stroke, AF diagnosed post-stroke, and non-AF stroke (no AF diagnosed pre-/post-stroke). We determined use of cardiovascular medications in the 90 days pre- and post-stroke and calculated mortality rates. RESULTS: There were 3201 hospitalised IS cases: 76.2% non-AF stroke, 15.7% AF pre-stroke, and 8.1% AF post-stroke. Oral anticoagulant (OAC) use increased between the pre- and post-stroke periods as follows: 54.3%-78.7% (AF pre-stroke group), 2.3%-84.8% (AF post-stroke group), and 3.4%-7.3% (non-AF stroke group). Corresponding increases in antiplatelet use were 30.8%-35.4% (AF pre-stroke group) 38.5%-47.5% (AF post-stroke group), and 37.5%-87.3% (non-AF stroke group). Among all IS cases, antihypertensive use increased from 66.8% pre-stroke to 78.8% post-stroke; statin use increased from 49.6%-85.2%. Mortality rates per 100 person-years (95% CI) were 17.30 (14.70-20.35) in the AF pre-stroke group and 9.65 (8.81-10.56) among all other stroke cases. CONCLUSION: Our findings identify areas for improvement in clinical practice, including optimising the level of OAC prescribing to patients with known AF, which could potentially help reduce the future burden of stroke.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".