Abstract 4: Variation and Temporal Trends in the Characteristics of US Hospitals Treating Acute Ischemic Stroke Patients with IV-tPA: Findings from GWTG-Stroke
Bibliographic record
Abstract
Introduction: Utilization of IV tPA is challenging for many hospitals. Using data from the national Get With The Guidelines-Stroke program, we analyzed changes over time in the characteristics of the hospitals that treated patients with tPA. Methods: We analyzed patient-level data from 2003-2011 at 1600 GWTG hospitals that joined the program at any time during the study period and admitted any acute ischemic stroke (AIS) patients arriving ≤ 2 hr of onset and eligible for tPA. Descriptive trends by time were analyzed by chi-square or Wilcoxon test for continuous data. Results: IV tPA was given within 3 hr at 1394 sites to 50,798/ 75,115 (67.6%) eligible AIS patients arriving ≤ 2 hr; 206 (14.8%) sites had a least one eligible patients but no tPA use. IV tPA treatment rates varied substantially across hospitals (median 61.2%, range 0-100%), with > 200 hospitals providing tPA < 10% of the time (Figure). Over time, more patients and a larger proportion of patients were treated at smaller (median bed size 407 vs. 372, p< 0.001), non-academic, Southern hospitals, and those with lower annualized average ischemic stroke volumes (252.4 vs. 235.2, p< 0.001) (Table). While more than half of all tPA patients were treated at Primary Stroke Centers, this proportion did not change over time. The proportion of patients treated at high volume tPA treatment sites (average > 20/year) increased over time (31.9 vs. 34.5, p< 0.007). Conclusion: Over the past decade, while primary stroke centers still account for more than half of all treatments, tPA has been increasingly delivered in smaller, non-academic hospitals. These data support the continued emphasis on stroke team building and systems of care at US hospitals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".