Temporal Trends in Patient Characteristics and Treatment With Intravenous Thrombolysis Among Acute Ischemic Stroke Patients at Get With the Guidelines-Stroke Hospitals
Bibliographic record
Abstract
Substantial efforts over the past decade have increased rates of intravenous tissue plasminogen activator (tPA) use in the United States. We sought to determine changes in patient characteristics and rates of tPA use over time among hospitalized acute ischemic stroke (AIS) patients.We analyzed all AIS patients (n=1 093 895) and those arriving ≤ 2 hours and treated with tPA ≤ 3 hours after onset (n=50 798) from 2003 to 2011 in the American Heart Association's Get with the Guideline-Stroke (GWTG-Stroke). Categorical data were analyzed by Pearson χ(2) and continuous data by Wilcoxon test. Intravenous tPA use ≤ 3 hours after onset increased from 4.0% to 7.0% in all AIS admissions and 42.6% to 77.0% in AIS patients arriving ≤ 2 hours and fully eligible for tPA (P<0.001). In univariate analysis, tPA use increased over time, especially in those aged >85 years, nonwhite, and with milder strokes (National Institutes of Health Stroke Scale 0-4). Door-to-image time (median 24 versus 20 minutes) and door-to-tPA time (median 81 versus 72 minutes) also improved, with ≈65% of tPA-treated patients getting brain imaging ≤ 25 minutes after arrival. Multivariable analysis showed that with each additional calendar year, the odds that an eligible patient would receive tPA increased by 1.37-fold, adjusting for other covariates.The frequency of IV tPA use among all AIS patients, regardless of contraindications, nearly doubled from 2003 to 2011. Treatment with tPA has expanded to include more patients with mild deficits, nonwhite race/ethnicity, and oldest old age.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".