Relationship Between Magnetic Resonance Arterial Patency and Perfusion-Diffusion Mismatch in Acute Ischemic Stroke and Its Potential Clinical Use
Bibliographic record
Abstract
BACKGROUND: In patients with acute ischemic stroke the magnetic resonance (MR) perfusion-diffusion mismatch pattern (perfusion lesion at least 20% larger than the lesion on diffusion-weighted imaging) may indicate ischemically threatened but viable tissue. To our knowledge, the relationship of this MR pattern to serial changes in MR angiography (MRA) has not been reported. OBJECTIVES: To investigate the relationship between MRA changes and patterns of diffusion-weighted imaging and perfusion abnormalities and to determine if the information obtained could be used in clinical management. METHODS: The MR studies of 35 patients who had undergone sequential multimodality MR imaging studies within the first 4 days of stroke were reviewed. All lesions were in the internal carotid artery territory distribution. Magnetic resonance angiographies were read by 2 observers blinded to the clinical data. RESULTS: During the first 24 hours a perfusion-diffusion mismatch was present in 22 (92%) of the 24 patients with an MRA arterial occlusive lesion. (At this time 5 [46%] of the 11 patients with a normal MRA [P =.006] also had a mismatch.) Two to 4 days after stroke, of these 22 patients resolution of the mismatch occurred in 8 (87%) of 9 patients with recanalization on MRA compared with 5 (39%) of 13 patients without arterial recanalization (P =.03). Resolution of mismatch occurred in 3 (60%) of 5 patients with a normal MRA and a mismatch at the first time point. CONCLUSIONS: Concordance between MRA and the MR perfusion-diffusion mismatch pattern provides supportive evidence for an arterial vascular basis for this MR signature in acute stroke. Discordance between MRA lesions and mismatch may result from arterial branch occlusions undetected by MRA or from an alternate mechanism for the mismatch. The MR imaging patterns identified extend our understanding of the pathophysiology of stroke and may contribute to the improvement of stroke management in the future.
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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 | 0.000 | 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".