A System-Based Intervention to Improve Access to Hyperacute Stroke Care
Bibliographic record
Abstract
BACKGROUND: Hyperacute stroke is a time-sensitive emergency for which outcomes improve with faster treatment. When stroke systems are accessed via emergency medical services (EMS), patients are routed to hyperacute stroke centres and are treated faster. But over a third of patients with strokes do not come to the hospital by EMS, and may inadvertently arrive at centres that do not provide acute stroke services. We developed and studied the impact of protocols to quickly identify and move "walk-in" patients from non-hyperacute hospitals to regional stroke centres (RSCs). METHODS AND RESULTS: Protocols were developed by a multi-disciplinary and multi-institutional working group and implemented across 14 acute hospital sites within the Greater Toronto Area in December of 2012. Key metrics were recorded 18 months pre- and post-implementation. The teams regularly reviewed incident reports of protocol non-adherence and patient flow data. Transports increased by 80% from 103 to 185. The number of patients receiving tissue plasminogen activator (tPA) increased by 68% from 34 to 57. Total EMS transport time decreased 17 minutes (mean time of 54.46 to 37.86 minutes, p<0.0001). Calls responded to within 9 minutes increased from 34 to 59%. CONCLUSIONS: A systems-based approach that included a multi-organizational collaboration and consensus-based protocols to move patients from non-hyperacute hospitals to RSCs resulted in more patients receiving hyperacute stroke interventions and improvements in EMS response and transport times. As hyperacute stroke care becomes more centralized and endovascular therapy becomes more broadly implemented, the protocols developed here can be employed by other regions organizing patient flow across systems of stroke care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".