Factors associated with decreasing diffusion-weighted imaging-positive area volume after mechanical thrombectomy in patients with large early ischemic changes
Bibliographic record
Abstract
Objectives This study aimed to evaluate the factors associated with decreasing diffusion-weighted imaging (DWI) positive areas in patients with large early ischemic changes after mechanical thrombectomy (MT). Materials and Methods This retrospective single-center clinical study was conducted between January 2013 and December 2022. We included consecutive patients who underwent MT for acute large-vessel occlusion of the anterior circulation with low pretreatment DWI-Alberta Stroke Program Early Computed Tomography Scores (ASPECTS) (0–5), effective recanalization [thrombolysis in cerebral infarction (TICI) 2b or TICI3], and magnetic resonance imaging (MRI) acquired before and after MT. We measured the DWI-positive area volume before and after MT. The primary endpoint was the after/before-MT DWI-positive area–volume ratio. Results In total, 28 patients were included in this study. Eight patients (29%) had an after/before-MT DWI-positive area–volume ratio of <1. The median mean apparent diffusion coefficient (ADC) levels of the DWI-positive areas in the groups with a ratio of <1 or >1 were 717 × 10⁶ mm 2 /s and 637 × 10 6 mm 2 /s, respectively ( p = 0.011). Multivariate logistic regression analysis showed that ADC level (OR, 1.020 [95% confidence intervals (CIs), 1.001–1.040]; p = 0.040) was an independent predictor of a decreased DWI-positive area after MT. There was a negative correlation between the mean ADC level and the after/before-MT DWI-positive area–volume ratio ( p < 0.001, | ρ| = 0.650), and the mean pretreatment ADC cutoff level was 649 × 10 6 mm 2 /s (area under the curve (AUC) = 0.806) for predicting a volume ratio of <1. Conclusions The mean ADC level before-MT correlated with the after/before-MT DWI-positive area–volume ratio. A mean pretreatment ADC cutoff level of 649 × 10 6 mm 2 /s predicted a decreased DWI-positive area after MT.
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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 | 0.000 | 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 | 0.000 |
| 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 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".