(ASPECTS) for Assessing CT Scans in Patients with Acute Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Clinicians are insecure reading CT scans by using the one-third rule for acute middle cerebral artery stroke (1/3 MCA rule) before treating patients with recombinant tissue plasminogen activator. The 1/3 MCA rule is a poorly defined volumetric estimate of the size of cerebral infarction of the MCA. A 10-point quantitative topographic CT scan score, the Alberta Stroke Program Early CT Score (ASPECTS), is described and illus-trated. A sharp increase in dependence and death occurs with an ASPECTS of 7 or less. We describe how to use ASPECTS and why it works with CT scans obtained on all commonly used axial baselines. We also describe interobserver reliability among clinicians from different specialties and with different experience in reading CT scans in the context of acute stroke. METHODS: The six physicians who developed ASPECTS answered a questionnaire on pre-cisely how they interpret and use ASPECTS. The ASPECTS areas as interpreted by these physicians were compared with one another and with standards in the literature. k statistics were used to assess the interobserver reliability of ASPECTS versus the 1/3 MCA rule. RESULTS: The exact methods of interpretation varied among the six individual observers, with either a 3:3 or 4:2 split on the specific questions. The overall interobserver agreement was
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".