What ASPECTS Value Best Predicts the 100-mL Threshold on Diffusion Weighted Imaging? Study of 150 Patients with Middle Cerebral Artery Stroke
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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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 | 0.003 |
| 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 | 0.001 | 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".