Presence of diabetic microvascular complications does not incrementally increase risk of ischemic stroke in diabetic patients with atrial fibrillation
Bibliographic record
Abstract
Conventional stroke risk prediction tools used in atrial fibrillation (AF) incorporate the presence of diabetes mellitus (DM) as a risk factor. However, it is unknown whether this risk is homogenous or dependent on the presence of diabetic microvascular complications, such as diabetic retinopathy, nephropathy, and neuropathy. The present study examined the risk of ischemic stroke in diabetic patients with and without microvascular complications. The present study used the National Health Insurance Research Database in Taiwan with detailed healthcare data on all-comers to the Taiwanese medical system from January 1, 1996 to December 31, 2011. AF and DM were identified when listed as discharge diagnoses or confirmed more than twice in the outpatient department. Patients on antithrombotic agents were excluded. The clinical endpoint was ischemic stroke. Among the 50,180 AF patients with DM, the majority had no microvascular complications (72.7%), while 2.6% had diabetic retinopathy, 8.4% had diabetic nephropathy, and 16.1% had diabetic neuropathy. Ischemic stroke occurred in 6003 patients, with a 4.74% annual risk of ischemic stroke. When compared with DM patients without microvascular complications, those with diabetic retinopathy, nephropathy, or neuropathy had higher incidences of ischemic stroke (4.65 vs 5.07, 4.77, or 5.20 per 100 person-years, respectively). However, after adjusting for confounding factors, the differences were no longer significant. In a large nationwide AF cohort with DM, risk of ischemic stroke was similar between patients with and without microvascular complications, suggesting that risk stratification of these patients does not require inclusion of diabetic retinopathy, nephropathy, and neuropathy.
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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 | 0.004 |
| 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) | 0.001 | 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".