P.065 Emergency medical services activation Following Face, Arm, Speech, Time (FAST) public awareness campaigns in Quebec, Canada
Bibliographic record
Abstract
Background: Face, Arm, Speech, Time (FAST) campaigns improve stroke recognition in the general population. We assessed the effect of five consecutive FAST campaigns on emergency medical services (EMS) calls for suspected strokes in Quebec, Canada. Methods: We compared with t-tests the daily EMS call volume changes in the greater Montreal area before and after five FAST campaigns held between 2015 and 2019. We used interrupted time-series to measure changes in EMS daily call volume for suspected strokes following each FAST campaign (all calls, calls <5 hours from symptom onset, calls rated 3/3 on the Cincinnati Prehospital Stroke Scale [CPSS]) and used calls for acute headaches as a comparator. Results: After five FAST campaigns, mean daily calls increased by 28% (p<0.001) for suspected strokes, compared to 10% for acute headaches (p=0.012). Significant increases in daily stroke calls were only observed after three campaigns (highest OR=1.26, 95% CI: 1.11, 1.43; p<0.001). There were no significant changes in calls after individual campaigns for strokes <5 hours from symptom onset and 3/3 CPSS strokes. Conclusions: The individual effect of FAST campaigns on daily stroke calls to EMS was inconsistent. Further refinement of FAST campaigns may help improve prompt EMS activation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".