Collaboration and Partnerships with EMS to enhance the stroke system in Northwestern Ontario.
Bibliographic record
Abstract
Background:Canadian Stroke Best Practice Recommendations states that approximately two-thirds of all patients who seek acute care for stroke in Canada arrive at the Emergency Department by Ambulance. The current target for transport to hospital by paramedics is 80% of cases. In Northwestern Ontario (NWO), only 49% of all patients who seek stroke acute care arrive at the Emergency Department by ambulance with the Ontario provincial average being 64.8%. NWO communities are spread across 458,010 kilometers. Residents need to know the urgency of stroke care, understand that time lost is brain lost and that the paramedics play a key role in the critical first hours of stroke care. Methods:Since 2015, the Northwestern Ontario Regional Stroke Network (NWORSN) and EMS collaborate and partner to build awareness, increase knowledge and helps save lives of stroke in NWO. Projects include Advanced Stroke Education at the EMS training sessions; First Responder teams trained in Stroke Education; EMS and Stroke Neurologist Question and Answer You Tube video produced. FAST decals project in partnership with Heart and Stroke Foundation. Results:Stroke Education to EMS trained 190 paramedics. 12 first responder teams provided training, totally 113 first responders. You Tube video provided to 4 EMS services for staff education. All 4 EMS services received FAST decals, totally 77 ambulances. Conclusions:The NWORSN has strong partnerships with EMS resulting in improved access to EMS for stroke patients, increased knowledge for paramedics and first responders and continued collaboration and coordination of system optimization and stroke care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".