Abstract 173: Patterns and Predictors of Emergency Medical Services Pre-Notification of Potential Stroke Cases in the United States: Findings from GWTG-Stroke
Bibliographic record
Abstract
Background: Emergency medical services (EMS) pre-notification of potential stroke arrivals has been recommended as a means of improving stroke evaluation and treatment times. However, little is known as to how frequently EMS pre-notification is being applied in the US, how use varies by hospital/state/region, and factors associated with EMS pre-notification. Methods : Acute ischemic stroke patients transported by EMS to 1585 GWTG-Stroke hospitals from April 2003 to March 2011 were studied. Patient and hospital characteristics associated with EMS pre-notification were analyzed with multivariate GEE models. Results: Of 371,988 acute ischemic stroke patients transported by EMS, pre-notification occurred in 249,197 (67.0%). Among hospitals with at least 10 EMS arriving stroke patients (n=1395), the median rate of pre-notification was 70.0 (25 th -75 th 34.0-92.9%, range 0%-100%). There was significant variation in pre-notification by state ranging from a low of 19.7% in Washington DC to a high of 93.4% in Montana. EMS pre-notification rates non-significantly increase over time, 58.0% in 2003 to 67.3% in 2011, p=0.10. Patient factors independently associated with EMS pre-notification include younger age, white race, no diabetes, and history of atrial fibrillation (Table). Hospital factors included region (West and Midwest), non-academic status, and higher annual IV tPA volumes (Table). Conclusions: EMS pre-notification is provided in only two-thirds of EMS arriving GWTG-Stroke patients ultimately diagnosed with acute ischemic stroke in the US and varies substantially by hospital, state, and region in the US. Older patients, non-white, and those with certain comorbid conditions were significantly less likely to have EMS pre-notification. These findings suggest there are further opportunities to improve EMS pre-notification rates.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".