Diffusion- and Perfusion-Weighted MRI Patterns in Borderzone Infarcts
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The pathophysiology of borderzone infarcts is not well understood. We investigated whether combined diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) could identify pathophysiologically meaningful categories of borderzone infarcts. METHODS: Seventeen patients with borderzone infarcts were identified from the Beth Israel Deaconess Medical Center Stroke Database. All patients had DWI and PWI, the majority of them within the first 24 hours of symptom onset. RESULTS: Three patterns of perfusion abnormalities were associated with the diffusion lesions: 1, normal perfusion (5 patients); 2, localized perfusion deficits matching the area of restricted diffusion (5 patients); and 3, extensive perfusion deficits involving 1 or more vascular territories (7 patients). All but 1 patient with pattern 1 had transient peri-infarct hypotension as the presumed stroke mechanism. Two patients with pattern 2 had cardiac or aortic embolic sources; none had large-artery disease or arterial hypotension. Reperfusion was detected in all patients with this pattern who submitted to a follow-up study. All patients with pattern 3 had severe stenosis or occlusion of a large artery: the internal carotid, anterior cerebral, or middle cerebral. CONCLUSIONS: We postulate that the perfusion abnormality varies according to the mechanism of the borderzone infarction. Transient perfusion deficits occurring with hypotension in the absence of significant large-artery disease may not be revealed by PWI. Embolism may cause some cases of small borderzone perfusion deficits. Critical large-artery disease may cause large territorial perfusion deficits and predispose to borderzone infarction.
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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) | 0.005 | 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".