Increase in Endovascular Therapy in Get With The Guidelines-Stroke After the Publication of Pivotal Trials
Bibliographic record
Abstract
BACKGROUND: Beginning in December 2014, a series of pivotal trials showed that endovascular thrombectomy (EVT) was highly effective, prompting calls to reorganize stroke systems of care. However, there are few data on how these trials influenced the frequency of EVT in clinical practice. We used data from the Get With The Guidelines-Stroke program to determine how the frequency of EVT changed in US practice. METHODS: We analyzed prospectively collected data from a cohort of 2 437 975 patients with ischemic stroke admitted to 2222 participating hospitals between April 2003 and the third quarter of 2016. Weighted linear regression with 2 linear splines and a knot at January 2015 was used to compare the slope of the change in EVT use before and after the pivotal trials were published. Potentially eligible patients were defined as last known well to arrival time ≤4.5 hours and National Institutes of Health Stroke Scale score ≥6. RESULTS: The frequency of EVT use was slowly increasing before January 2015 but then sharply accelerated thereafter. In the third quarter 2016, EVT was provided to 3.3% of all patients with ischemic stroke at all hospitals, representing 15.1% of all patients who were potentially eligible for EVT based on stroke duration and severity. At EVT-capable hospitals, 7.5% of all patients with ischemic stroke were treated in the third quarter of 2016, including 27.3% of the potentially eligible patients. From 2013 to 2016, case volumes nearly doubled at EVT-capable hospitals. Mean case volume per EVT-capable hospital was 37.6 per year in the last 4 quarters. EVT case volumes increased in nearly all US states from 2014 to the last 4 quarters, but with persistent geographic variation unexplained by differences in potential patient eligibility. CONCLUSIONS: EVT use is increasing rapidly; however, there are still opportunities to treat more patients. Reorganizing stroke systems to route patients to adequately resourced EVT-capable hospitals might increase treatment of eligible patients, improve outcomes, and reduce disparities.
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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 | 0.100 | 0.373 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".