287Diabetes mellitus is a risk factor for ischemic stroke in patients with and without coronary artery disease after coronary angiography
Bibliographic record
Abstract
Abstract Background Diabetes (DM) and non-DM patients without coronary artery disease (CAD) by coronary angiography (CAG) have the same low risk of myocardial infarction. Purpose To study whether DM patients without CAD have the same risk of ischemic stroke as patients with neither DM nor CAD. Methods We conducted a cohort study of patients, who underwent CAG between 2004 and 2012 in the Western Denmark Heart Registry. Patients previously diagnosed with ischemic stroke or atrial fibrillation (AF) and those treated with an oral anticoagulant were excluded. Patients were stratified according to presence of DM and CAD. Follow-up started 30 days after CAG. We computed event rates and adjusted incidence rate ratios (IRRs) using patients with neither DM nor CAD as reference. Results A total of 68,829 patients were included. Median follow-up was 4.0 years. Patients with both DM and CAD were at the highest risk of ischemic stroke (1.25 events per 100 person-years; adjusted IRR 2.10, 95% CI 1.77–2.48) (Figure 1). Patients with CAD alone (0.70 events per 100 person-years; adjusted IRR 1.29, 95% CI 1.12–1.48) or DM alone (0.84 events per 100 person-years; adjusted IRR 1.79, 95% CI 1.41–2.26) were at intermediate risk while patients with neither DM nor CAD (0.46 events per 100 person-years) were at lowest risk. Among DM patients, extent of CAD was further predictive of risk (ptrend<0.001). Figure 1 Conclusions Not only CAD but also DM independently predict the risk of ischemic stroke after CAG. Their combination further increases the risk of ischemic stroke depending on the extent of CAD.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".