Usefulness of MRA‐DWI mismatch in neuroendovascular therapy for acute cerebral infarction
Bibliographic record
Abstract
BACKGROUND: This study evaluated the usefulness of MR angiography (MRA)-diffusion-weighted imaging (DWI) mismatch in neuroendovascular therapy over 3 h after onset of acute cerebral infarction. METHODS: The subjects were 14 cases (age, 73 ± 8.4 years) who had an anterior circulation deficit on DWI/MRA on arrival and underwent neuroendovascular therapy over 3 h after onset. MRA-DWI mismatch (MDM) (+) was defined as 'major artery lesion (+) and diffusion-weighted image-Alberta Stroke Program Early CT Score (DWI-ASPECTS) ≥6'; MDM (-) was defined as 'major artery lesion (+) and DWI-ASPECTS <6'. RESULTS: Reperfusion was achieved in nine of 14 patients (64%) undergoing neuroendovascular therapy. Within the reperfusion group, in the five MDM (+) patients and the four MDM (-) patients, the outcome was a favorable clinical response in the MDM (+) group. The modified Rankin Scale (mRS) scores after 90 days were 0-2 in 3 (60%) and 3-6 in 2 (40%) of the MDM (+) group patients and 0-2 in 0 (0%) and 3-6 in 4 (100%) of the MDM (-) group patients. In the MDM (+) group, a good outcome was achieved. However, the number of cases was small, so this was not a significant difference. Within the non-reperfusion group, in the three MDM (+) patients and the two MDM (-) patients, the mRS scores after 90 days were 0-2 in 1 (33%) and 3-6 in 2 (67%) of the MDM (+) group patients and 0-2 in 0 (0%) and 3-6 in 2 (100%) of the MDM (-) group patients. In both groups, the outcome was poor. CONCLUSIONS: With neuroendovascular therapy, a good outcome with reperfusion was achieved in the MDM (+) group compared to the MDM (-) group. This suggests that the presence or absence of MDM may be useful in determining prognosis after reperfusion.
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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) | 0.000 | 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) | 0.000 | 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".