CT Angiographic Source Images Predict Outcome and Final Infarct Volume Better Than Noncontrast CT in Proximal
Bibliographic record
Abstract
Background and Purpose—Alberta Stroke Programme Early CT Score (ASPECTS) is widely used for assessment of early ischemic changes in acute stroke. We hypothesized that CT angiography source image (CTA-SI) ASPECTS correlates better with baseline National Institutes of Health Stroke Scale score, final ASPECTS and neurological outcomes when compared with noncontrast CT ASPECTS. Methods—We studied patients presenting with acute ischemic stroke and identified proximal arterial occlusions (internal carotid artery, middle cerebral artery M1, and proximal middle cerebral artery M2) from the Calgary CT Angiography database. CT scans were independently read by 3 observers for baseline noncontrast CT ASPECTS, CT angiography source image ASPECTS, and follow-up ASPECTS. Details of demographics and risk factors were noted. A modified Rankin Scale score 2 at 3 months was considered a favorable outcome. Results—We identified 261 patients with proximal occlusions for analysis. We found a better correlation between CT angiography source image ASPECTS and follow-up ASPECTS (Spearman correlation coefficient r0.65; 95 % CI, 0.58 to 0.72; P0.001) than between noncontrast CT ASPECTS and follow-up CT ASPECTS (r0.46; 95 % CI, 0.36 to 0.55; P0.001). CT angiography source image ASPECTS correlated better with baseline National Institutes of Health Stroke Scale and 24-hour National Institutes of Health Stroke Scale when compared with noncontrast CT ASPECTS (P0.001). In an adjusted model including both CT angiography source image ASPECTS and noncontrast CT ASPECTS,
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".