Abstract 203: Use of Endovascular Therapy and Trends in Clinical Outcomes within the Nationwide Get With The Guidelines-Stroke Registry
Bibliographic record
Abstract
Purpose: To determine hospital and patient level characteristics associated with use of endovascular therapy for acute ischemic stroke and to analyze trends in clinical outcome. Methods: Data were from Get With The Guidelines-Stroke hospitals from 4/1/2003 to 6/30/2013. We looked at secular trends in number of hospitals providing endovascular therapy, use of endovascular therapy in these hospitals, and clinical outcomes. We also analyzed hospital and patient characteristics associated with endovascular therapy utilization. Results: Of 1087 hospitals, 454 provided endovascular therapy to at least one patient in the study period. From 2003 to 2012, the proportion of hospitals providing endovascular therapy increased by 1.6%/year (from 12.9% to 28.9%), with a modest drop in 2013 to 23.4%. Use in these hospitals increased from 0.7% to 2% of all ischemic stroke patients (p<0.001) with a modest drop in 2013 to 1.9%. In multivariable analyses, patient outcomes after endovascular therapy improved over time, with reductions in in-hospital mortality (29.6% in 2004 to 16.2% in 2013; p=0.002); and from late 2010, reduction in symptomatic intracranial hemorrhage (ICH) (11% in 2010 to 5% in 2013; p<0.0001) and increased independent ambulation at discharge (24.5% in 2010 to 33% in 2013; p<0.0001) and discharge home (17.7% in 2010 to 26.1% in 2013; p<0.0001) (Attached figure). Hospital characteristics associated with endovascular therapy use included large size, teaching status and urban location while patient characteristics included younger age, EMS transport, absence of prior stroke and white race. Conclusion: Use of endovascular therapy increased modestly in this national registry from 2003 to 2012 and decreased in 2013. Clinical outcomes improved notably from 2010 to 2013, coincident with the introduction of newer thrombectomy devices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".