Vascular Hyperintensity on Fluid-Attenuated Inversion Recovery Indicates the Severity of Hypoperfusion in Acute Stroke
Bibliographic record
Abstract
BACKGROUND AND AIM: Although fluid-attenuated inversion recovery vascular hyperintensities may be frequently seen in acute large-artery ischemic stroke, reports on their prognostic utility had been conflicting due to lack of quantitative evaluation of the perfusion status based on the signal intensity. We hypothesized that greater hyperintensity represents more severe hypoperfusion. METHODS: Overall, 27 patients with acute occlusion of the proximal middle cerebral artery were divided into 2 groups, based on their signal intensity in the insular segment of middle cerebral artery on the affected side, relative to that of the insular cortex: the low signal intensity group (hypo- or isointense signals, n = 12) and the high signal intensity group (hyperintense signals, n = 15). Using dynamic susceptibility contrast magnetic resonance imaging, we assessed the time of the maximum value of the residue function and mean transit time, in the entire middle cerebral artery cortical area and diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score regions, including the corona radiata. RESULTS: The high signal intensity group had significantly longer time of the maximum value of the residue function in all the diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score regions, except the M3 and M6 regions, and significantly longer mean transit time in the M1 and M4 regions. CONCLUSIONS: Quantitative analysis of the perfusion parameters revealed more severely compromised and widely disturbed perfusion status in the high signal intensity group than in the low signal intensity group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".