Accuracy of Published Screening Tools for Large Vessel Occlusion in Patients With Suspected Acute Ischemic Stroke: A Prospective Cohort Study
Bibliographic record
Abstract
STUDY OBJECTIVE: To identify the most accurate screening tool for predicting a large vessel occlusion in patients with a suspected acute ischemic stroke. METHODS: Between January 2022 and April 2023, adult patients with a suspected acute ischemic stroke for whom an emergency physician activated the stroke code (indicating potential eligibility for thrombolysis and/or thrombectomy) at the emergency department (ED) of l'Hôpital de l'Enfant-Jésus-CHU de Québec, a tertiary care center for neurologic diseases, were prospectively included. Demographic data and variables included in 8 screening tools were collected by the emergency physician prior to the head computed tomography using a standardized data collection form. The performance of each tool to identify patients with a large vessel occlusion was assessed using the accuracy with 95% confidence intervals (CIs) and the McNemar test was used to compare the performance of the tools. RESULTS: A total of 390 patients were included in the study (mean age: 72.3 years; men: 48.2%). Acute ischemic strokes was the final diagnosis in 259 patients (66.4%) of which 111 (28.5%) had a large vessel occlusion. The accuracy of Field Assessment Stroke Triage for Emergency Destination (FAST-ED) was 0.76 (95% CI 0.72 to 0.81), which was not significantly different from that of Rapid Arterial Occlusion Evaluation Scale (0.75, 95% CI 0.71 to 0.80), Los Angeles Motor Scale (0.75, 95% CI 0.71 to 0.79), or Large ARtery Intracranial Occlusion stroke scale (0.72, 95% CI 0.68 to 0.77). However, it was significantly higher than the accuracy of Conveniently-Grasped FAST, Ambulance Clinical Triage-FAST, Vision, Aphasia, Neglect assessment, and Face-Arm-Speech-Time plus severe arm or leg motor deficit. Cincinnati Prehospital Stroke Scale, when performed by either the emergency physicians or paramedics, demonstrated poor accuracy, with values of 0.34 (95% CI 0.29 to 0.39) and 0.37 (95% CI 0.32 to 0.34), respectively. CONCLUSION: This study provides valuable insights into the accuracy of various large vessel occlusion screening tools for patients in our ED setting with FAST-ED, Rapid Arterial Occlusion Evaluation Scale, and Los Angeles Motor Scale showing the highest levels of accuracy. These findings will contribute to the development of evidence-based care pathways for improving stroke diagnosis and management.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".