What ASPECTS Value Best Predicts the 100-mL Threshold on Diffusion Weighted Imaging? Study of 150 Patients with Middle Cerebral Artery Stroke
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Bibliographic record
Abstract
PURPOSE: Infarct volume ≥100 mL on diffusion weighted imaging (DWI) predicts symptomatic hemorrhagic transformation and poor outcome. Our aim was to determine the correlation between the Alberta Stroke Program Early CT Score (ASPECTS) and infarct volume and to identify the optimal value for describing infarcts ≥100 mL. METHODS: This was a retrospective study of acute infarcts isolated to the middle cerebral artery territory imaged by DWI <48 hours from ictus. Two neuroradiologists blinded to volumetric measurements assigned ASPECTS while a third observer used a semi-automated thresholding technique to determine infarct volume. Correlation of ASPECTS and infarct volume was determined using Spearman's rank coefficient (ρ). Receiver-operating characteristics (ROC) curve analysis was performed to identify the optimal ASPECTS for ≥100 mL. RESULTS: One hundred and fifty patients were evaluated; the median and range for infarct volumes were 32.3 and 10.0-277 mL, respectively. The median and range for ASPECTS were 7 and 1-9, respectively. A strong correlation was found with ρ=-.807 (P < .0001). 22 (14.7%) infarcts were ≥100 mL and the area under the ROC curve was .976 (P < .0001). The optimal ASPECTS was ≤3 with sensitivity and specificity of 77.3% and 97.7%, respectively. CONCLUSION: ASPECTS may serve as a surrogate marker of infarct extent on DWI.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it