Validity of Acute Stroke Lesion Volume Estimation by Diffusion-Weighted Imaging–Alberta Stroke Program Early Computed Tomographic Score Depends on Lesion Location in 496 Patients With Middle Cerebral Artery Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) has been used to estimate diffusion-weighted imaging (DWI) lesion volume in acute stroke. We aimed to assess correlations of DWI-ASPECTS with lesion volume in different middle cerebral artery (MCA) subregions and reproduce existing ASPECTS thresholds of a malignant profile defined by lesion volume ≥100 mL. METHODS: We analyzed data of patients with MCA stroke from a prospective observational study of DWI and fluid-attenuated inversion recovery in acute stroke. DWI-ASPECTS and lesion volume were calculated. The population was divided into subgroups based on lesion localization (superficial MCA territory, deep MCA territory, or both). Correlation of ASPECTS and infarct volume was calculated, and receiver-operating characteristics curve analysis was performed to identify the optimal ASPECTS threshold for ≥100-mL lesion volume. RESULTS: A total of 496 patients were included. There was a significant negative correlation between ASPECTS and DWI lesion volume (r=-0.78; P<0.0001). With regards to lesion localization, correlation was weaker in deep MCA region (r=-0.19; P=0.038) when compared with superficial (r=-0.72; P<0.001) or combined superficial and deep MCA lesions (r=-0.72; P<0.001). Receiver-operating characteristics analysis revealed ASPECTS≤6 as best cutoff to identify ≥100-mL DWI lesion volume; however, positive predictive value was low (0.35). CONCLUSIONS: ASPECTS has limitations when lesion location is not considered. Identification of patients with malignant profile by DWI-ASPECTS may be unreliable. ASPECTS may be a useful tool for the evaluation of noncontrast computed tomography. However, if MRI is used, ASPECTS seems dispensable because lesion volume can easily be quantified on DWI maps.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".