MR Perfusion and Diffusion in Acute Ischemic Stroke: Human Gray and White Matter have Different Thresholds for Infarction
Bibliographic record
Abstract
It is thought that gray and white matter (GM and WM) have different perfusion and diffusion thresholds for cerebral infarction in humans. We sought to determine these thresholds with voxel-by-voxel, tissue-specific analysis of co-registered acute and follow-up magnetic resonance (MR) perfusion- and diffusion-weighted imaging. Quantitative cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and apparent diffusion coefficient (ADC) maps were analyzed from nine acute stroke patients (imaging acquired within 6 h of onset). The average values of each measure were calculated for GM and WM in normally perfused tissue, the region of recovered tissue and in the final infarct. Perfusion and diffusion thresholds for infarction were determined on a patient-by-patient basis in GM and WM separately by selecting thresholds with equal sensitivities and specificities. Gray matter has higher thresholds for infarction than WM (P<0.009) for CBF (20.0 mL/100 g min in GM and 12.3 mL/100 g min in WM), CBV (2.4 mL/100 g in GM and 1.7 mL/100 g in WM), and ADC (786 x 10(-6) mm(2)/s in GM and 708 x 10(-6) mm(2)/s in WM). The MTT threshold for infarction in GM is lower (P=0.014) than for WM (6.8 secs in GM and 7.1 secs in WM). A single common threshold applied to both tissues overestimates tissue at risk in WM and underestimates tissue at risk in GM. This study suggests that tissue-specific analysis of perfusion and diffusion imaging is required to accurately predict tissue at risk of infarction in acute ischemic stroke.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".