Characteristics, Performance Measures, and In-Hospital Outcomes of the First One Million Stroke and Transient Ischemic Attack Admissions in Get With The Guidelines-Stroke
Bibliographic record
Abstract
BACKGROUND: Stroke results in substantial death and disability. To address this burden, Get With The Guideline (GWTG)-Stroke was developed to facilitate the measurement, tracking, and improvement in quality of care and outcomes for acute stroke and transient ischemic attack (TIA) patients in the United States. METHODS AND RESULTS: We analyzed the characteristics, performance measures, and in-hospital outcomes in the first 1 000 000 acute ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage, and TIA admissions from 1392 hospitals that participated in the GWTG-Stroke Program 2003 to 2009. Patients were 53.5% women, 73.3% white, and with mean age of 70.1+/-14.9 years. There were 601 599 (60.2%) ischemic strokes, 108 671 (10.9%) intracerebral hemorrhages, 34 945 (3.5%) subarachnoid hemorrhages, 26 977 (2.7%) strokes not classified, and 227 788 (22.8%) TIAs. Performance measures showed small to moderate differences by cerebrovascular event type. In-hospital mortality rate was highest among intracerebral hemorrhage (25.0%) and subarachnoid hemorrhage (20.4%), and intermediate in ischemic stroke (5.5%) patients and lowest among TIA patients (0.3%). Significant improvements over time from 2003 to 2009 in quality of care were observed: all-or-none measure, 44.0% versus 84.3% (+40.3%, P<0.0001). After adjustment for patient and hospital variables, the cumulative adjusted odds ratio for the all-or-none measure over the 6 years was 9.4 (95% confidence interval, 8.3 to 10.6, P<0.0001). Temporal improvements in length of stay and risk-adjusted in-hospital mortality rate (for ischemic stroke and TIA) were also observed. CONCLUSIONS: With more than 1 million patients enrolled, GWTG-Stroke represents an integrated stroke and TIA registry that supports national surveillance, innovative research, and sustained quality improvement efforts facilitating evidence-based stroke/TIA care.
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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 | 0.007 |
| 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 | 0.001 | 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".