Early successful reperfusion after endovascular therapy reduces malignant middle cerebral artery infarction occurrence in young patients with large diffusion‐weighted imaging lesions
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Malignant middle cerebral artery infarction (MMI) is a severe complication of acute ischaemic stroke (AIS). The aim of our study was to assess whether successful reperfusion after endovascular therapy (EVT) in AIS with clinical and imaging predictors of MMI decreased its occurrence. METHODS: Data were collected between January 2014 and July 2018 in a monocentric prospective AIS registry of patients treated with EVT. Patients selected were <65 years old with severe anterior circulation AIS with a National Institutes of Health Stroke Scale score >15, baseline Diffusion-Weighted Imaging-Alberta Stroke Program Early Computed Tomography Score ≤ 6 and baseline diffusion-weighted imaging lesion volume >82 mL within 6 h of symptom onset. Successful reperfusion was defined as a Thrombolysis in Cerebral Ischemia score ≥ 2b. Occurrence of MMI was the primary endpoint. RESULTS: A total of 66 EVT-treated patients were included in our study. MMI occurred in 27 patients (41%). In unadjusted analysis, successful reperfusion was associated with fewer MMIs (31.8% vs. 65.0%; P = 0.015) and with more favorable outcome at 3 months (50% vs. 20%; P = 0.023). In multivariate analysis, successful reperfusion was associated with an adjusted odds ratio (95% confidence intervals) of 0.35 (0.10-1.12) for MMI and 2.77 (0.84-10.43) for 3-month favorable outcome occurrence. CONCLUSIONS: Early successful reperfusion performed in patients with AIS with clinical and imaging predictors of MMI was associated with decreased MMI occurrence. Reperfusion status might be considered in evaluating the need for craniectomy in patients with early predictors of MMI.
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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 | 0.004 |
| 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 | 0.001 | 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".