Abstract WP292: Changing Landscape of Stroke Systems of Care: Increase in Inter Hospital Transfer in the United States
Bibliographic record
Abstract
Purpose: Interhospital transfer is necessary to facilitate endovascular therapy (EVT) for patients with emergent large vessel occlusion (ELVO) who present to nonendovascular centers. We hypothesized that interhospital transfer would accelerate following 2014 pivotal randomized controlled trials (RCTs) demonstrating benefits of EVT. Methods: We analyzed trend of interhospital transfer for EVT using Get With The Guideline (GWTG)-Stroke from January 2012 to September 2016. Analyses of transfer-in trend for the hospitals consistently providing EVT were restricted to hospitals with >1 EVT/quarter in last 4 study quarters (250 sites). We analyzed trend of interhospital transfer for all ischemic stroke (IS) patients and following subgroup: NIHSS ≥6 and arrival to the EVT-providing hospital within 7 hours of last known well. Results: During the study period 31425 patients received EVT. Transfer-in EVT cases increased from 334 to 912 from Q3 2014 to Q3 2016 (p<0.001 for change in linear trend, Figure 1A). Interhospital transfer for EVT is common (44% in Q3 2016) and increasing proportion of IS patients are arriving to EVT-providing hospitals as transfers, especially among the subgroup of NIHSS ≥6 and arrival to the EVT-providing hospital within 7 hours of last known well (Figure 1B). Since RCT announcement, transfer-in patients receive EVT at a significantly higher rate (5.2% in Q3 2014 vs. 10.8% in Q3 2016, Figure 1C). Hospitals with a higher % transfer-in EVT (comparing top quartile to bottom quartile) were more likely to have more beds and be in an urban location, with comprehensive stroke center certification and higher annual EVT volumes. Conclusions: Interhospital transfer of IS patients has accelerated markedly in the past two years, highlighting the need to develop protocols and quality metrics to ensure efficient systems of care for this subset of 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.005 | 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".