Beyond FAST: Evaluating the Impact of Expanded Stroke Criteria on Emergency Response
Bibliographic record
Abstract
Abstract Background In Ireland, the ACT FAST campaign has been a central strategy to raise public awareness of stroke symptoms, with initiatives in 2010, 2015, and 2023. While the FAST (Face, Arm, Speech, Time) acronym targets core anterior circulation symptoms, the expanded BE-FAST criteria include Balance and Eye symptoms to improve detection of posterior circulation strokes. This study evaluates whether expanding symptom recognition improves timely hospital presentation and treatment access. Methods A retrospective review of a stroke register at a single Irish centre was conducted, analysing patients who presented between January and December 2022. Data collected included demographics, mode of arrival (self vs ambulance), symptom type (FAST or BE-FAST), and time from symptom onset to hospital arrival. Symptom classification was based on clinical documentation. Results Of 316 stroke patients, 74.4% were FAST-positive and 87% were BE-FAST-positive. While BE-FAST improved overall sensitivity, FAST-positive patients were significantly more likely to arrive by ambulance (p = 0.035). Only speech symptoms were independently associated with both earlier presentation and a higher likelihood of receiving endovascular treatment (EVT) (p = 0.021). No significant time-to-treatment benefit was found for BE-FAST-positive patients compared to FAST-positive alone. Conclusion Although the BE-FAST criteria identify more patients with stroke symptoms, they do not appear to meaningfully enhance time-to-treatment or increase ambulance use. Speech symptoms remain the most predictive of early arrival and intervention. These findings suggest that while broader symptom messaging increases sensitivity, continued emphasis on the core FAST symptoms—especially speech—may be more effective in prompting timely presentation and 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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".