Quality of Care and Outcomes for In-Hospital Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Analysis of quality of care for in-hospital stroke has not been previously performed at the national level. This study compares patient characteristics, process measures of quality, and outcomes for in-hospital strokes with those for community-onset strokes in a national cohort. METHODS: We performed a retrospective cohort study of the Get With The Guidelines-Stroke (GWTG-Stroke) database of The American Heart Association from January 2006 to April 2012, using data from 1280 sites that reported ≥1 in-hospital stroke. Patient characteristics, comorbid illnesses, medications, quality of care measures, and outcomes were analyzed for 21 349 in-hospital ischemic strokes compared with 928 885 community-onset ischemic strokes. RESULTS: Patients with in-hospital stroke had more thromboembolic risk factors, including atrial fibrillation, prosthetic heart valves, carotid stenosis, and heart failure (P<0.0001), and experienced more severe strokes (median National Institutes of Health Stroke Score 9.0 versus 4.0; P<0.0001). Using GWTG-Stroke achievement measures, the proportion of patients with defect-free care was lower for in-hospital strokes (60.8% versus 82.0%; P<0.0001). After accounting for patient and hospital characteristics, patients with in-hospital strokes were less likely to be discharged home (adjusted odds ratio 0.37; 95% confidence intervals [0.35-0.39]) or be able to ambulate independently at discharge (adjusted odds ratio 0.42; 95% confidence intervals [0.39-0.45]). In-hospital mortality was higher for in-hospital stroke (adjusted odds ratio 2.72; 95% confidence intervals [2.57-2.88]). CONCLUSIONS: Compared with community-onset ischemic stroke, patients with in-hospital stroke experienced more severe strokes, received lower adherence to process-based quality measures, and had worse outcomes. These findings suggest there is an important opportunity for targeted quality improvement efforts for patients with in-hospital stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".