Electronic Alberta Stroke Program Early CT score change and functional outcome in a drip-and-ship stroke service
Bibliographic record
Abstract
Background Debate continues as to whether patients with acute ischemic stroke with (suspected) large vessel occlusion benefit from direct referral versus secondary transportation. Aims To analyze the change in early infarct signs, collaterals, and acute ischemia volume and their association with transfer time and functional outcome. Methods We retrospectively analyzed consecutive transfers between 2013 and 2016 for patients with anterior circulation stroke transported from referring hospitals to our center as potential candidates for thrombectomy. Alberta Stroke Programme Early CT Scores (ASPECTS) were automatically calculated on external and in-house CT using the Brainomix e-ASPECTS software, and collaterals were assessed using the e-CTA tool. Functional status after stroke using the modified Rankin scale (mRS) was obtained. Results 102 patients with CT scans both at the referring hospital and our center were identified. During patient transfer, e-ASPECTS declined by a median of 1 point (0–2). Functional outcome correlated with the change in e-ASPECTS (decline, n=54) (Spearman r s =0.322, 95% CI 0.131 to 0.482, p=0.001). The median image-to-image time was 149 min (IQR 113–190), but did not correlate with change in e-ASPECTS (p=0.754) and mRS score at 3 months (p=0.25). Preserved good collateral status assessed at the comprehensive stroke center was associated with better functional outcome ( r s =−0.271, 95% CI −0.485 to −0.037, p=0.02). Conclusions Patient transfer in a drip-and-ship network was associated with declines in e-ASPECTS associated with worse functional outcome. Image-to-image time did not influence this association, but worsening collateral status did.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".