Risk Factor Profile and Management of Cerebrovascular Patients in the REACH Registry
Bibliographic record
Abstract
BACKGROUND: Cerebrovascular disease (CVD) is a global public health problem. CVD patients are at high risk of recurrent stroke and other atherothrombotic events. Prevalence of risk factors, comorbidities, utilization of secondary prevention therapies and adherence to guidelines all influence the recurrent event rate. We assessed these factors in 18,992 CVD patients within a worldwide registry of stable outpatients. METHODS: The Reduction of Atherothrombosis for Continued Health Registry recruited >68,000 outpatients (44 countries). The subjects were mainly recruited by general practitioners (44%) and internists (29%) if they had symptomatic CVD, coronary artery disease, peripheral arterial disease (PAD) and/or >or=3 atherothrombotic risk factors. RESULTS: The 18,992 CVD patients suffered a stroke (53.7%), transient ischemic attack (TIA) (27.7%) or both (18.5%); 40% had symptomatic atherothrombotic disease in >or=1 additional vascular beds: 36% coronary artery disease; 10% PAD and 6% both. The prevalence of risk factors at baseline was higher in the TIA subgroup than in the stroke group: treated hypertension (83.5/82.0%; p = 0.02), body mass index >or=30 (26.7/20.8%; p < 0.0001), hypercholesterolemia (65.1/52.1%; p < 0.0001), atrial fibrillation (14.7/11.9%; p < 0.0001) and carotid artery disease (42.3/29.7%; p < 0.0001). CVD patients received antiplatelet agents (81.7%), oral anticoagulants (17.3%), lipid-lowering agents (61.2%) and antihypertensives (87.9%), but guideline treatment targets were frequently not achieved (54.5% had elevated blood pressure at baseline, while 4.5% had untreated diabetes). CONCLUSIONS: A high percentage of CVD patients have additional atherothrombotic disease manifestations. The risk profile puts CVD patients, especially the TIA subgroup, at high risk for future atherothrombotic events. Undertreatment is common worldwide and adherence to guidelines needs to be enforced.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".