Abstract TMP49: Increasing Prevalence of Cerebrovascular Risk Factors in Native Americans With Ischemic Stroke
Bibliographic record
Abstract
Introduction: Prior epidemiological studies have reported ethnic differences in cerebrovascular risk factors. There is a paucity in the literature detailing the burden of these risk factors in the Native American population. We aim to elucidate the risk factor prevalence in the Native Americans with IS. Methods: Data were extracted from the Cerner electronic health record database which is a nationwide database of about 700 hospitals across the United States. We retrospectively identified IS patients encompassing all ages who were of Native American ethnicity during the years 2000-2016. Demographic parameters and cerebrovascular risk factors were collected. Results: Of the 5540 patients belonging to Native American ethnicity diagnosed with stroke from 2000 to 2016, 4729 had IS. Of these patients, 2175 (46%) were male, 3145 (66.6%) had a diagnosis of hypertension (HTN), 1834 (38.8%) had diabetes mellitus (DM), 1102 (23.4%) had coronary artery disease (CAD), 1020 (21.6%) were smokers, 583 (12.3%) had heart failure (HF), 495 (10.5%) had atrial fibrillation, and 65 (1.4%) had a diagnosis of atrial flutter. Trends in risk factors over time were analyzed using logistic regression model adjusted for age and gender, with year range of subgroup as the independent variable. Except for DM all other cerebrovascular risk factors including HF (p=0.02), HTN (p<0.001), CAD (p<0.015), atrial fibrillation (p<0.006), atrial flutter (p=0.01), and smoking (p<0.001) showed a statistically significant increasing prevalence during the years 2000 to 2016. Conclusion: We recognized increasing prevalence of cerebrovascular risk factors in Native American patients with ischemic stroke. These results highlight the importance of stroke prevention aimed at the Native American population by targeting individual cerebrovascular risk factors.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".