Risk for recurrent cardiovascular disease events among patients with diabetes and chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Adults who have experienced multiple cardiovascular disease (CVD) events have a very high risk for additional events. Diabetes and chronic kidney disease (CKD) are each associated with an increased risk for recurrent CVD events following a myocardial infarction (MI). METHODS: We compared the risk for recurrent CVD events among US adults with health insurance who were hospitalized for an MI between 2014 and 2017 and had (1) CVD prior to their MI but were free from diabetes or CKD (prior CVD), and those without CVD prior to their MI who had (2) diabetes only, (3) CKD only and (4) both diabetes and CKD. We followed patients from hospital discharge through December 31, 2018 for recurrent CVD events including coronary, stroke, and peripheral artery events. RESULTS: Among 162,730 patients, 55.2% had prior CVD, and 28.3%, 8.3%, and 8.2% had diabetes only, CKD only, and both diabetes and CKD, respectively. The rate for recurrent CVD events per 1000 person-years was 135 among patients with prior CVD and 110, 124 and 171 among those with diabetes only, CKD only and both diabetes and CKD, respectively. Compared to patients with prior CVD, the multivariable-adjusted hazard ratio for recurrent CVD events was 0.92 (95%CI 0.90-0.95), 0.89 (95%CI: 0.85-0.93), and 1.18 (95%CI: 1.14-1.22) among those with diabetes only, CKD only, and both diabetes and CKD, respectively. CONCLUSION: Following MI, adults with both diabetes and CKD had a higher risk for recurrent CVD events compared to those with prior CVD without diabetes or CKD.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".