Impact of Expanding the Prehospital Stroke Bypass Time Window in a Large Geographic Region
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Ontario Acute Stroke Medical Redirect Paramedic Protocol (ASMRPP) was revised to allow paramedics to bypass to designated stroke centers if total transport time would be <2 hours and total time from symptom onset <3.5 hours. We sought to evaluate the impact and safety of implementing the Revised ASMRPP. METHODS: We conducted a 12-month implementation study involving prehospital patients presenting with possible stroke symptoms. A total of 1317 basic and advanced life support paramedics, of 9 land services in 10 rural counties and 5 cities, used the Revised ASMRPP to take appropriate patients directly to 6 designated stroke centers. RESULTS: We enrolled 1277 patients with 98.8% paramedic compliance in form completion. Of these, 755 (61.2%) met the redirect criteria and had these characteristics: mean age 72.1 (range 16-101), male 51.1%, mean time scene to hospital 16.7 minutes (range 0-92). Paramedics demonstrated excellent interobserver agreement (κ, 0.94; 95% confidence interval, 0.91-0.96) and 97.9% accuracy in interpretation of the Revised ASMRPP. Prehospital adverse events occurred in 14.7% of patients, but few were life-threatening. Overall, 71.4% of 755 cases had a stroke code activated at the hospital and 23.2% received thrombolysis. For the 189 potential stroke patients picked up in 1 city, the ASMRPP classified thrombolysis administration with sensitivity 100% and specificity 37.3% and a final diagnosis of stroke, with sensitivity 86.1% and specificity 41.9%. CONCLUSIONS: In a large urban-rural area with 9 paramedic services, we demonstrated accurate, safe, and effective implementation of the Revised ASMRPP. These revisions will allow more patients with stroke to benefit from early treatment.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".