[Mismatch of ASPECTS based on arterial spin labeling and diffusion-weighted imaging as an indicator for mechanical thrombectomy in patients with wake-up stroke].
Bibliographic record
Abstract
OBJECTIVE: To retrospectively analyze the outcomes of wake-up stroke (WUS) patients with occlusion of large vessel occlusion (LVO), who were selected for mechanical thrombectomy according to the mismatch of Alberta Stroke Program Early CT Score (ASPECTS) based on arterial spin labeling (ASL) and diffusion-weighted image (DWI) on admission magnetic resonance (MR) scans. METHODS: Twelve consecutive WUS patients with acute LVO of the anterior circulation undergoing MR scans with ASL and DWI prior to thrombectomy were retrospectively evaluated. The mismatch of ASPECTS was defined as the difference between ASL-ASPECTS and DWI-ASPECTS, and a higher score indicates a greater mismatch. RESULTS: The procedures led to successful reperfusion in all the cases (Thrombolysis in Cerebral Infarction Grade 2b-3). Eleven patients (91.7%) had significantly decreased National Institute of Health Stroke scale (NIHSS) score at discharge.AmRS score of ≤2 at 90 days was achieved in 8 of the 12 patients (66.7%). CONCLUSIONS: The mismatch between ASPECTS assessed based on ASL and DWI can detect a true mismatch in patients with acute LVO of the anterior circulation, and can be used for rapid screening of patients eligible for thrombectomy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".