Fluid-attenuated inversion recovery vascular hyperintensity-diffusion-weighted imaging mismatch and functional outcome after endovascular reperfusion therapy for acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Fluid-attenuated inversion recovery vascular hyperintensity (FVH) outside of the diffusion-weighted imaging (DWI) lesion, termed FVH-DWI mismatch, may represent penumbral tissue with good collateral status. METHODS: Consecutive patients who underwent endovascular reperfusion therapy (EVT) for acute internal carotid artery (ICA) or middle cerebral artery (MCA)-M1 occlusion were enrolled. FVH-DWI mismatch score was defined as the number of cortical Alberta Stroke Program Early CT Score areas (I and M1 to M6) that involved FVH but no DWI lesion (0 to 7 points). The outcome measure was set as good functional outcome, defined as a modified Rankin Scale score of 0 to 2, at 90 days after onset. RESULTS: Of 196 consecutive patients who underwent EVT for acute ICA or MCA-M1 occlusion, 32 without brain MRI before EVT were excluded, and the remaining 164 were analyzed. The median FVH-DWI mismatch score was 2 (interquartile range, 0 to 4). At 90 days after EVT, 2 patients were lost-to follow-up, and 73 had good functional outcome. The frequency of good functional outcome at 90 days after EVT increased significantly with increasing FVH-DWI mismatch score (P for trend <0.001). FVH-DWI mismatch score was independently associated with good functional outcome at 90 days after onset (adjusted odds ratio per 1 point,1.46; 95% confidence interval, 1.15-1.89). CONCLUSIONS: Patients with large FVH-DWI mismatch had good functional outcome after EVT for acute ICA or MCA-M1 occlusion.
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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 | 0.000 | 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".