Abstract 1: Patient and Hospital Factors Associated with Lack of IV tPA Use among Eligible Patients with Acute Ischemic Stroke: Findings from Get with the Guidelines-Stroke
Bibliographic record
Abstract
BACKGROUND: Nationally representative studies suggest that many eligible patients are not treated with IV tPA. Even among hospitals participating in the Get With The Guidelines (GWTG)-Stroke, treatment rates remain suboptimal. We sought to determine patient and hospital characteristics independently associated with lack of tPA treatment among eligible patients in the GWTG registry. METHODS: We studied patients with ischemic stroke who arrived ≤2 hours of onset, were tPA-eligible, and had complete data at 1,839 GWTG hospitals from 4/1/03-12/31/11. Eligibility was determined by the lack of documented contraindications/warnings to tPA. Unadjusted and adjusted ORs (computed with GEE logistic regression models to account for hospital clustering of patients) are reported. Due to 28.4% missing NIHSS, GEE models were built with and without NIHSS. RESULTS: Of 75,115 tPA-eligible patients during the 9-year period, 50,798 (68%) were tPA-treated with median age of 73 years (IQR 60-82), 51% female, 74% non-Hispanic white, and median NIHSS 11. In multivariable models, lack of treatment was associated with older age, females, African-American race, prior diabetes, stroke, or prosthetic heart valve and not arriving by EMS (Table). Treatment was also less likely at hospitals that were rural, non-teaching, in the Midwest and South, and non-stroke centers, earlier in the study period, and at hospitals that did not routinely perform NIHSS (Table). Adding NIHSS to the model (c=0.77 vs. c=0.78) did not change model results substantially but did show reduced tPA use in patients with mild strokes (NIHSS 0-4 vs 5-9, OR 3.4; vs 10-14, OR 4.6; vs 15-20, OR 5.4; vs 21+, OR 4.5). CONCLUSIONS: One-third of eligible patients at GWTG hospitals did not receive IV tPA treatment. Several patient and hospitals factors were independently associated with this lack of treatment. These findings can inform system interventions to improve tPA treatment among eligible patients.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".