Abstract 214: Improvement Among All Hospital Types and Reduced Hospital Level Variation in Use of Intravenous tPA for Acute Ischemic Stroke 2003-2011: Findings from Get With The Guideline-Stroke
Bibliographic record
Abstract
Introduction: IV tPA delivery is challenging and use varies widely. We analyzed differences in patient and hospital characteristics at the hospital level in the Get with the Guidelines (GWTG) - Stroke database. Methods: We analyzed data on 73574 patients from 2003-2011 at 1231 hospitals with ≥10 tPA-eligible ischemic stroke (AIS) patients arriving < 2 hr of onset, divided into quartiles of rates of tPA delivered within 3 hrs of onset. Median percentages are reported, and temporal trends were calculated using absolute changes from 2010-2011 vs. 2003-2005. Results: Patients at hospitals with lower rates of tPA treatment within 3 hrs were older, more frequently white, used EMS less often, had lower NIHSS values with very high rates of missing NIHSSS, and greater door to imaging times as compared to better performing hospitals. Hospitals with lower rates of tPA treatment were smaller and more rural, had fewer ICU beds, and were less often teaching or primary stroke centers (Table 1). IV tPA use increased across all types of hospitals from 2003-2011, but increased to a greater degree in non-primary stroke centers and those in the South and West (Table 2). Teaching status, bed size and other measured variables were not different. Conclusion: Significant increases in IV tPA treatment among patients arriving < 2hr have occurred over the past decade, and rates of increase vary by hospital characteristics. The profile of tPA treated eligible patients also changes across the range of hospital tPA use rates, with highest performing sites reporting NIHSS in >90% of tPA patients, and treating greater numbers of patients who are non-white or with more severe strokes. Low performing sites may benefit from greater focus on NIHSS assessment and timeliness of care. .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".