Use of the Alberta Stroke Program Early CT Score (ASPECTS) for assessing CT scans in patients with acute stroke.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 illustrated. 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 precisely 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. kappa 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 good compared with that of the 1/3 MCA rule. Normal anatomic vascular and interobserver variations explain why ASPECTS can be applied with different CT axial baselines. CONCLUSION: ASPECTS is a systematic, robust, and practical method that can be applied to different axial baselines. Clinician agreement is superior to that of the 1/3 MCA rule.
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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 | 0.000 |
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 it