ASPECTS (Alberta Stroke Program Early CT Score) Assessment of the Perfusion–Diffusion Mismatch
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Rapid and reliable assessment of the perfusion-weighted imaging (PWI)/diffusion-weighted imaging (DWI) mismatch is required to promote its wider application in both acute stroke clinical routine and trials. We tested whether an evaluation based on the Alberta Stroke Program Early CT Score (ASPECTS) reliably identifies the PWI/DWI mismatch. METHODS: A total of 232 consecutive patients with acute middle cerebral artery stroke who underwent pretreatment magnetic resonance imaging (PWI and DWI) were retrospectively evaluated. PWI-ASPECTS and DWI-ASPECTS were determined blind from manually segmented PWI and DWI volumes. Mismatch-ASPECTS was defined as the difference between PWI-ASPECTS and DWI-ASPECTS (a high score indicates a large mismatch). We determined the mismatch-ASPECTS cutoff that best identified the volumetric mismatch, defined as VolumeTmax>6s/VolumeDWI≥1.8, a volume difference≥15 mL, and a VolumeDWI<70 mL. RESULTS: Inter-reader agreement was almost perfect for PWI-ASPECTS (κ=0.95 [95% confidence interval, 0.90-1]), and DWI-ASPECTS (κ=0.96 [95% confidence interval, 0.91-1]). There were strong negative correlations between volumetric and ASPECTS-based assessments of DWI lesions (ρ=-0.84, P<0.01) and PWI lesions (ρ=-0.90, P<0.01). Receiver operating characteristic curve analysis showed that a mismatch-ASPECTS ≥2 best identified a volumetric mismatch, with a sensitivity of 0.93 (95% confidence interval, 0.89-0.98) and a specificity of 0.82 (95% confidence interval, 0.74-0.89). CONCLUSIONS: The mismatch-ASPECTS method can detect a true mismatch in patients with acute middle cerebral artery stroke. It could be used for rapid screening of patients with eligible mismatch, in centers not equipped with ultrafast postprocessing software.
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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 | 0.002 | 0.006 |
| 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".