Abstract WP242: An International Survey of Progressive Stroke System Processes
Bibliographic record
Abstract
Introduction: Understanding of high performing stroke systems can help development of regional stroke systems of care and bring meaningful system improvement. We sought to evaluate the current state of advanced systems worldwide to inform the development of a large-scale regional system of care improvement program. Methods: An 81 question, internet-based survey was developed to obtain information on current practices in large and progressive stroke systems. Data was collected from January 2017 - May 2017. Advanced stroke systems were identified by prior knowledge and colleague input. A total of 22 stroke centers from Europe and North America were invited to participate. The survey included questions encompassing center demographics, staffing, acute stroke code processes, Endovascular therapy (EVT) processes, EMS systems, stroke IT, telestroke use, transfer of patients, post-acute patient management and system feedback. Data analysis was conducted using REDCap internal analytic tools; simple counts and averages (mean) were calculated where appropriate. Results: Of the 22 invited centers, 9 (41%; 2 European, 1 Canadian, 6 US) completed the questionnaire, 7 of which were CSCs. Responding centers reported yearly average of 887 ischemic strokes (range 350-1444), and 202 (100-501) primary hemorrhagic strokes. Average annual IV tPA volume was 168 (60-440) and EVT volume was 68 (12-130). At all 9 systems, EMS agencies were trained in identifying potential strokes and reported utilizing an established prehosptial stroke scale. Six systems (66.7%) reported a destination protocol based on stroke severity involving bypass to an EVT-capable center. All 9 centers have developed processes for expedited transfer of stroke patients in need of neurointervention from their non-EVT-capable referral hospital network. For patients with confirmed large vessel occlusion being transferred to the hub hospital, 5 (55.6%) centers reported these patients bypass the ED to go directly to the neurointervention suite. Conclusions: This international survey of progressive stroke systems reveals useful practice patterns and processes that can be adopted by other stroke systems to improve patient care. This information will be integrated into the IMPROVE Stroke program.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".