P.025 Accuracy of code stroke activations: a tale of two comprehensive stroke centres
Bibliographic record
Abstract
Background: We evaluated the accuracy of code strokes activations at two comprehensive stroke centres in Toronto, Canada. Methods: We conducted a multi-centre, retrospective cohort study of all adult patients seen as code stroke in emergency rooms (ER) of two comprehensive stroke centres (CSC) in Toronto, Canada between January 1, 2022 and Dec 31, 2022. We included cases where the code stroke was activated in the field by paramedics and where it was activated in the ER by a physician. We reported off-criteria code stroke activations as the proportion of code stroke activations that did not meet all criteria for activation, and described the criteria that were not met. Results: A total of 677 (61.9% paramedic) code strokes were seen at CSC1 and 439 (80.6% paramedic) at CSC2. At CSC1, 21.2% paramedic-activated and 38.6% ER-activated were off-criteria, and at CSC2, 14.2% paramedic-activated and 48.1% ER-activated code stroke were off-criteria. Most of these were due to incorrect assessment of the last seen normal time. Conclusions: One in five code strokes did not meet criteria for activation. Improving the accuracy of paramedic and ER assessment of last seen normal time may be an avenue to reduce off-criteria code stroke activations.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".