Alberta Stroke Program Early Computed Tomographic Scoring Performance in a Series of Patients Undergoing Computed Tomography and MRI
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In this study, we compare the performance of pretreatment Alberta Stroke Program Early Computed Tomographic scoring (ASPECTS) using noncontrast CT (NCCT) and MRI in a large endovascular therapy cohort. METHODS: Prospectively enrolled patients underwent baseline NCCT and MRI and started endovascular therapy within 12 hours of stroke onset. Inclusion criteria for this analysis were evaluable pretreatment NCCT, diffusion-weighted MRI (DWI), and 90-day modified Rankin Scale scores. Two expert readers graded ischemic change on NCCT and DWI using the ASPECTS. ASPECTS scores were analyzed with the full scale or were trichotomized (0-4 versus 5-7 versus 8-10) or dichotomized (0-7 versus 8-10). Good functional outcome was defined as a 90-day modified Rankin Scale score of 0 to 2. RESULTS: Seventy-four patients fulfilled our study criteria. The full-scale inter-rater agreement for CT-ASPECTS and DWI-ASPECTS was 0.579 and 0.867, respectively. DWI-ASPECTS correlated with functional outcome (P=0.004), whereas CT-ASPECTS did not (P=0.534). Both DWI-ASPECTS and CT-ASPECTS correlated with DWI volume. The receiver operating characteristic analysis revealed that DWI-ASPECTS outperformed both CT-ASPECTS and the time interval between symptom onset and start of the procedure for predicting good functional outcome (modified Rankin Scale score, ≤2) and DWI volume ≥70 mL. CONCLUSION: Inter-rater agreement for DWI-ASPECTS was superior to that for CT-ASPECTS. DWI-ASPECTS outperformed NCCT ASPECTS for predicting functional outcome at 90 days.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".