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 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 | 0.003 |
| 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 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".