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 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) | 0.000 | 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 | 0.000 | 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".