ASPECTS on CTA Source Images Versus Unenhanced CT
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Alberta Stroke Program Early CT Score (ASPECTS) is a grading system to assess ischemic changes on CT in acute ischemic stroke. CT angiography-source images (CTA-SI) predict final infarct volume. We examined whether the final infarct ASPECTS and clinical outcome were more related to acute CTA-SI ASPECTS than to the acute noncontrast CT (NCCT) ASPECTS. METHODS: ASPECTS was assigned by 2 raters on the acute NCCT, CTA-SI, and follow-up imaging. The mean baseline ASPECTS of acute NCCT and CTA-SI was compared with the follow-up ASPECTS. Rate ratios (RRs) were used to quantify the relationship between the dichotomized baseline ASPECTS (categorized as 0 to 7 versus 8 to 10) and favorable patient outcome. RESULTS: Thirty-nine patients were recruited. Proximal occlusion (internal carotid artery or middle cerebral artery) was seen in 62%, M2 occlusion in 18%, and no occlusion was seen in 20% of patients. The median time between symptom onset and imaging was 1.9 (1.2 to 2.5) hours. There was a significantly larger difference of 1.4 between the mean baseline NCCT and CTA-SI ASPECTS in patients who had more ischemic changes (follow-up ASPECTS=0 to 3) than a difference of 0.6 in patients who had near-to-normal CT scans (follow-up ASPECTS=8 to 10). The rate of favorable outcome for acute NCCT ASPECTS of 8 to 10 was 51.8% versus 25.0% for 0 to 7 (RR, 2.1, 95% CI: 0.7 to 5.9, P=0.12). For acute CTA-SI ASPECTS of 8 to 10, the rate of favorable outcome was 58.8% versus 31.8% for 0 to 7 (RR, 1.8, 95% CI: 0.9 to 3.8, P=0.09). CONCLUSIONS: CTA-SI ASPECTS provides added information in the prediction of final infarct size.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".