Antithrombotic Management after an Ischemic Stroke in French Primary Care Practice: Results from Three Pooled Cross-Sectional Studies
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We aimed at quantifying and explaining the underuse of antithrombotic treatments after an ischemic stroke in patients seen in French primary care. METHODS: We pooled all ischemic stroke patients included in 3 observational primary care-based observational studies. French general practitioners and cardiologists recruited 14,544 patients with atherothrombotic disease including 4,322 with an ischemic stroke. Antithrombotic therapies and risk factors were prospectively recorded. Patients with atrial fibrillation (AF) were considered appropriate for oral anticoagulants (OAC) and those without AF for antiplatelet drugs. RESULTS: Out of the 4,322 stroke patients, 3,732 (86.3%) were taking at least one antithrombotic drug. Among the 765 patients with AF, 333 (43.5%) received OAC and 2,718 (86.9%) out of the 3,129 patients appropriate for antiplatelet drug were taking antiplatelet drug. Multivariate analyses did not single out any risk factors for nonuse of OAC and showed that female sex (OR = 1.48; IC 95%: 1.14-1.92) was associated with nonuse of antiplatelet drugs. Conversely, past myocardial infarction (OR = 0.44; IC 95%: 0.26-0.71) and hypercholesterolemia (OR = 0.64; IC 95%: 0.50-0.81) were associated with appropriate use of antiplatelet drugs. CONCLUSION: More than 50% of stroke patients with AF do not receive OAC and 15% of those without AF do not receive antiplatelet drugs. These findings are not satisfactorily explained by the main patients' characteristics and practitioner's speciality and underline the complexity of the process which allows the transfer of scientific evidence in clinical practice.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".