Identification of Penumbra and Infarct in Acute Ischemic Stroke Using Computed Tomography Perfusion–Derived Blood Flow and Blood Volume Measurements
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Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We investigated whether computed tomography (CT) perfusion-derived cerebral blood flow (CBF) and cerebral blood volume (CBV) could be used to differentiate between penumbra and infarcted gray matter in a limited, exploratory sample of acute stroke patients. METHODS: Thirty patients underwent a noncontrast CT (NCCT), CT angiography (CTA), and CT perfusion (CTP) scan within 7 hours of stroke onset, NCCT and CTA at 24 hours, and NCCT at 5 to 7 days. Twenty-five patients met the criteria for inclusion and were subsequently divided into 2 groups: those with recanalization at 24 hours (n=16) and those without (n=9). Penumbra was operationally defined as tissue with an admission CBF <25 mL x 100 g(-1) x min(-1) that was not infarcted on the 5- to 7-day NCCT. Logistic regression was applied to differentiate between infarct and penumbra data points. RESULTS: For recanalized patients, CBF was significantly lower (P<0.05) for infarct (13.3+/-3.75 mL x 100 g(-1) x min(-1)) than penumbra (25.0+/-3.82 mL x 100 g(-1) x min(-1)). CBV in the penumbra (2.15+/-0.43 mL x 100 g(-1)) was significantly higher than contralateral (1.78+/-0.30 mL x 100 g(-1)) and infarcted tissue (1.12+/-0.37 mL x 100 g(-1)). Logistic regression using an interaction term (CBFxCBV) resulted in sensitivity, specificity, and accuracy of 97.0%, 97.2%, and 97.1%, respectively. The interaction term resulted in a significantly better (P<0.05) fit than CBF or CBV alone, suggesting that the CBV threshold for infarction varies with CBF. For patients without recanalization, CBF and CBV for infarcted regions were 15.1+/-5.67 mL x 100 g(-1) x min(-1) and 1.17+/-0.41 mL x 100 g(-1), respectively. CONCLUSIONS: We have shown in a limited sample of patients that CBF and CBV obtained from CTP can be sensitive and specific for infarction and should be investigated further in a prospective trial to assess their utility for differentiating between infarct and penumbra.
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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 it