ASPECT Scoring to Estimate >1/3 Middle Cerebral Artery Territory Infarction
Bibliographic record
Abstract
PURPOSE: To compare the inter-observer reliability of Alberta Stroke Programme Early CT Scoring (ASPECTS) with the ICE (Idealize-Close-Estimate) method of estimating > 1/3 middle cerebral artery territory (MCAT) infarction amongst stroke neurologists and to determine how well ASPECT Scoring predicts > 1/3 MCAT infarctions in acute ischemic stroke (AIS). BACKGROUND: The European Cooperative Acute Stroke Study suggested that > 1/3 involvement of the MCAT on early CT scan was a risk factor for symptomatic intracerebral hemorrhage (SICH) following treatment with tissue plasminogen activator (tPA) for AIS but, in the absence of a systematic method of estimation had poor interobserver reliability (Kappa 0.49). The ICE method was developed to standardize the approach to estimating early MCAT infarct size and has very good interobserver reliability (Kappa 0.72). ASPECTS has comparable interobserver reliability and is reported to predict both neurological outcome and SICH. METHODS: Five stroke neurologists were tested with 40 AIS CT scans. Each performed blinded independent assessments of early ischemic changes with both ASPECTS and ICE. The reference standard was majority opinion of 1/3 MCAT determination of five neuroradiologists. A receiver operator curve (ROC) was constructed and likelihood ratios (LR) were calculated. Chance corrected agreement (kappa) and chance independent agreement (phi) were calculated for both methods, and analysis of variance was used to calculate reliability by intraclass correlation coefficient (ICC) for ASPECTS. RESULTS: The LR for a positive test (> 1/3 MCAT) were extremely large and conclusive (approaching infinity) for ASPECTS of 0-3; were large and conclusive (30, 20, and 10) for ASPECTS of 4, 5, and 6 respectively; was an unhelpful 1 for ASPECTS of 7, and were again extremely large and conclusive (approaching zero) for ASPECTS of 8-10. A ROC plot supported an ASPECTS cutoff of < 7 as best for 1/3 MCAT estimation (94% sensitivity and 98% specificity). Kappa and Phi statistics were moderately good for both ASPECTS and ICE (0.7). ICC for ASPECTS was 0.8. CONCLUSIONS: When experienced stroke neurologists utilize a formalized method of quantifying early ischemic changes on CT, either ASPECTS or ICE, the interobserver agreement and reliability are satisfactory. ASPECTS allows for a strong and conclusive estimation of the presence of 1/3 MCAT involvement and a cutoff point of < 7 results in best test performance.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".