Patterns of Care Quality and Prognosis Among Hospitalized Ischemic Stroke Patients With Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Relatively little is known about the quality of care and outcomes for hospitalized ischemic stroke patients with chronic kidney disease (CKD). We examined quality of care and in-hospital prognoses among patients with CKD in the Get With The Guidelines-Stroke (GWTG-Stroke) program METHODS AND RESULTS: We analyzed 679 827 patients hospitalized with ischemic stroke from 1564 US centers participating in the GWTG-Stroke program between January 2009 and December 2012. Use of 7 predefined ischemic stroke performance measures, composite "defect-free" care compliance, and in-hospital mortality were examined based on glomerular filtration rate (GFR) categorized as a dichotomous (+CKD as <60) or rank-ordered variable: normal (≥ 90), mild (≥ 60 to <90), moderate (≥ 30 to <60), severe (≥ 15 to <30), and kidney failure (<15 or dialysis). There were 236 662 (35%) ischemic stroke patients with CKD. Patients with severe renal dysfunction or failure were significantly less likely to receive guideline-based therapies. Compared with patients with normal kidney function (≥ 90), those with CKD (adjusted OR 0.91 [95% CI: 0.89 to 0.92]), moderate dysfunction (adjusted OR 0.94 [95% CI: 0.92 to 0.97]), severe dysfunction (adjusted OR 0.80 [95% CI: 0.77 to 0.84]), or failure (adjusted OR 0.72 [95% CI: 0.68 to 0.0.76]), were less likely to receive 100% defect-free care measure compliance. Inpatient mortality was higher for patients with CKD (adjusted odds ratio 1.44 [95% CI: 1.40 to 1.47]), and progressively rose with more severe renal dysfunction. CONCLUSIONS: Despite higher in-hospital mortality rates, ischemic stroke patients with CKD, especially those with greater severity of renal dysfunction, were less likely to receive important guideline-recommended therapies.
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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 | 0.006 |
| 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 | 0.001 |
| 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".