Clinical diffusion mismatch better discriminates infarct growth than mean transit time–diffusion weighted imaging mismatch in patients with middle cerebral artery–M1 occlusion and limited infarct core
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Our purpose was to compare clinical diffusion mismatch (CDM) and mean transit time (MTT)-diffusion mismatch as predictors of infarct growth in patients with proximal middle cerebral artery (MCA) occlusion and small infarct core on presentation. METHODS: Retrospective analysis of consecutive stroke patients with: (1) MCA-M1 occlusion; (2) MRI performed ≤10 h from symptoms onset; and (3) baseline MRI-diffusion weighted imaging (DWI) volume ≤25 mL. Definitions included: CDM=baseline National Institutes of Health Stroke Scale (NIHSS) score ≥8 and DWI volume ≤25 mL; MTT-DWI mismatch=visually assessed unthresholded MTT lesion ((MTT-DWI))/DWI) ≥20% and ≥10 mL larger than the DWI lesion; and significant infarct growth (>20% (≥5 mL) increase in infarct volume on follow-up). Uni-/multivariate analyses were performed to define the predictors of infarct growth. RESULTS: 63 stroke patients with MCA-M1 occlusions and MRI within 10 h of onset were evaluated. 20 patients were excluded on the basis of DWI volume >25 mL leaving 43 patients (mean age 75.8 years; median NIHSS=13) in the study cohort. On univariate analysis, larger admission DWI volume (p<0.0001), baseline NIHSS score ≥8 (p=0.001), lack of IV and/or endovascular treatment (p=0.021), glucose levels >125 mg/dL (p=0.024), poor CT angiography collaterals (p=0.046), and lower admission Alberta Stroke Program Early CT score (ASPECTS) (p=0.049) predicted infarct growth. Baseline NIHSS score ≥8 was the only independent predictor of stroke growth in the multivariate analysis (p=0.001). All patients had MTT-DWI mismatch >20%. There was no significant association between the amount of MTT-DWI mismatch and infarct growth (p=0.33). CONCLUSIONS: CDM is the most powerful predictor of infarct growth in patients with MCA-M1 occlusion and small infarct core. Most of these patients will have a significant oligemic MTT lesion regardless of admission NIHSS score.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".