First Automated Stroke Imaging Evaluation via Electronic Alberta Stroke Program Early CT Score in a Mobile Stroke Unit
Bibliographic record
Abstract
BACKGROUND: Recently, a mobile stroke unit (MSU) was shown to facilitate acute stroke treatment directly at the emergency site. The neuroradiological expertise of the MSU is improved by its ability to detect early ischemic damage via automatic electronic (e) evaluation of CT scans using a novel software program that calculates the electronic Alberta Stroke Program Early CT Score (e-ASPECTS). METHODS: The feasibility of integrating e-ASPECTS into an ambulance was examined, and the clinical integration and utility of the software in 15 consecutive cases evaluated. RESULTS: Implementation of e-ASPECTS onto the MSU and into the prehospital stroke management was feasible. The values of e-ASPECTS matched with the results of conventional neuroradiologic analysis by the MSU team. The potential benefits of e-ASPECTS were illustrated by three cases. In case 1, excluding early infarct signs supported the decision to directly perform prehospital thrombolysis. In case 2, in which stroke was caused by large-vessel occlusion, the high e-ASPECTS value supported the decision to initiate intra-arterial treatment and triage the patient to a comprehensive stroke center. In case 3, the e-ASPECTS value was 10, indicating the absence of early infarct signs despite pre-existing cerebral microangiopathy and macroangiopathy, a finding indicating the program's robustness against artefacts. CONCLUSIONS: This study on the integration of e-ASPECTS into the prehospital stroke management via a MSU showed for the first time that such integration is feasible, and aids both decision regarding the treatment option and the triage regarding the most appropriate target hospital.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".