The Alberta Stroke Program Early CT Score in Clinical Practice: What have We Learned?
Bibliographic record
Abstract
The introduction of brain imaging with computed tomography revolutionised the treatment of patients with acute ischaemic stroke. With the visual differentiation of haemorrhagic stroke from ischaemic stroke, thrombolytic therapy became feasible. The Alberta Stroke Program Early CT Score was devised to quantify the extent of early ischaemic changes in the middle cerebral artery territory on noncontrast computed tomography. With its systematic approach, the score is simple and reliable. However, the assessment of early ischaemic changes and Alberta Stroke Program Early CT scoring require training. The Alberta Stroke Program Early CT Score is a strong predictor of functional outcome. Furthermore, the effectiveness of intraarterial thrombolysis in patients with middle cerebral artery occlusion shows effect modification by the Alberta Stroke Program Early CT Score. This review summarises the Alberta Stroke Program Early CT Score methodology. We illustrate current knowledge regarding Alberta Stroke Program Early CT Score applied to clinical trials and comment on how Alberta Stroke Program Early CT Score may facilitate clinical treatment decision making and future trial design. Moreover, we introduce a modification of the Alberta Stroke Program Early CT Score methodology that disregards isolated cortical swelling, i.e. focal brain swelling without associated parenchymal hypoattenuation, as early ischaemic changes in the Alberta Stroke Program Early CT Score system.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".