Abstract TP24: The Classification of Stroke Ambulance Dispatches in a Mixed Rural Urban Stroke Ambulance Program
Bibliographic record
Abstract
Introduction: Mobile Stroke Units (MSUs) are proven to shorten the time between stroke recognition and thrombolysis resulting to better patient outcomes and are deemed safe and cost-effective. Recent trials demonstrated that a dispatch of a mobile stroke unit in addition to conventional ambulances is associated with lower patient disability at 90 days. Despite these proven advantages, there are few studies describing the nature of the calls that MSUs receive to aid in resource planning. This study aims to describe the types of calls that a mixed rural/urban Stroke Ambulance (SA), in Edmonton, Alberta, Canada received in a year. Methods: Stroke Ambulance activations from April 1, 2023 to March 31, 2024 were reviewed to determine the composition of calls and further classify them as to the following criteria: origin of call (metro, suburban, rural), decision of SA (stood down en-route, arrived on scene, SA transport) and thrombolysis decision. Results: Of 1030 dispatches 791 were stood down en route. There were 59 patients who received consultation and transport by the Stroke Ambulance of which 51 patients received thrombolysis. There were another 180 patients who received consultation without transport or thrombolysis. Urban patients made up 80% of dispatches with rural and suburban 20%. Conclusions: Stroke ambulances in a mixed rural/urban program require a high deployment rate due to a high rate of stand downs. The program provides potentially valuable consultation in the field to three times the number of patients that receive thrombolysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".