Intravenous thrombolysis for patients with reverse magnetic resonance angiography and diffusion‐weighted imaging mismatch: <scp>SAMURAI</scp> and <scp>NCVC</scp> rt‐<scp>PA</scp> Registries
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The characteristics of reverse magnetic resonance angiography and diffusion-weighted imaging (MRA-DWI) mismatch (RMM), defined as a large DWI lesion in the absence of major artery occlusion (MAO), remain unknown, especially in patients treated with intravenous recombinant tissue plasminogen activator (rt-PA). METHODS: Patients with stroke in the middle cerebral artery territory were included. Early ischaemic changes (EIC) were assessed with the Alberta Stroke Program Early CT Score on DWI (DWI-ASPECTS). All patients were divided into four groups based on the presence of MAO and a DWI-ASPECTS cut-off value of <7. RMM was defined as DWI-ASPECTS <7 without MAO. Clinical characteristics, symptomatic intracerebral hemorrhage (sICH) and favorable functional outcome (modified Rankin Scale score 0-2) at 90 days were compared amongst the four groups. RESULTS: Of the 486 patients enrolled (167 women, median age 74 years, median initial National Institutes of Health Stroke Scale score 13), reverse MRA-DWI mismatch was observed in 24 (5%). Of the clinical characteristics, cardioembolism was the only factor that was independently associated with RMM [odds ratio (OR) 5.49, 95% confidence interval (CI) 1.25-24.1]. Multivariable analyses revealed that patients with RMM more commonly had sICH than those with DWI-ASPECTS ≥ 7 irrespective of the presence (OR 5.44, 95% CI 1.13-26.1) or absence (13.1, 2.07-83.3) of MAO, and they had a more favorable functional outcome than those with DWI-ASPECTS < 7 plus MAO (7.45, 2.39-23.2). CONCLUSION: RMM was observed in 5% of patients treated with rt-PA and associated with cardioembolism. Patients with RMM may benefit from thrombolysis compared with those with EIC with MAO, although increment in the rate of sICH is a concern.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".