Quality of Care and Outcomes in Patients With Diabetes Hospitalized With Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diabetes is a common comorbid disease in stroke patients and has a strong influence on stroke-related outcomes, including stroke recurrence. We sought to examine the quality of care and in-hospital outcomes in patients with diabetes in the Get With the Guidelines-Stroke (GWTG-Stroke) program. METHODS: Data were obtained from 415 926 ischemic stroke patients from 1070 United States hospitals that participated in GWTG-Stroke between 2003 and 2008. We analyzed the relationships between diabetes and quality of care, in-hospital mortality, and discharge home using multivariable logistic regression. RESULTS: There were 130 817 (31%) ischemic stroke patients with diabetes. Quality of care received by patients with and without diabetes was similar except for intravenous recombinant tissue plasminogen activator (rt-PA) and cholesterol treatment. Fifty-four percent of patients with diabetes who arrived within 2 hours of onset received rt-PA compared to 60.8% of patients without diabetes (adjusted odds ratio [aOR], 0.83; 95% CI, 0.79-0.88). Almost 80% of patients with diabetes were discharged on cholesterol treatment compared to 71% of patients without diabetes (aOR, 1.40; 95% CI, 1.37-1.44). Diabetes patients were less likely to be discharged home (aOR, 0.80; 95% CI, 0.78-0.81) and had a higher risk of in-hospital death (aOR, 1.12; 95% CI, 1.08-1.15). CONCLUSIONS: Quality of care among patients with and without diabetes was similar except for rt-PA and cholesterol treatment. Diabetes was associated with worse stroke-related outcomes. Greater quality-improvement efforts to increase the use of rt-PA and other secondary prevention treatments in patients with diabetes are warranted.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".