Perfusion Mapping Using Computed Tomography Allows Accurate Prediction of Cerebral Infarction in Experimental Brain Ischemia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We have developed a dynamic CT method to measure absolute cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). In this study we evaluated the ability of CT-derived functional maps to detect infarction in a rabbit model of focal cerebral ischemia. METHODS: Sequential dynamic CT studies were performed at 2 different slices in 5 control rabbits and another 8 after induction of focal cerebral ischemia. The size of critically ischemic tissue was correlated to size of infarction measured by postmortem 2,3,5-triphenyltetrazolium chloride staining. In the control rabbits, short-term variability of the parameters was assessed by ANOVA analysis. RESULTS: In 7 of 8 animals of the ischemia group, cerebral infarction was visible on 2,3, 5-triphenyltetrazolium chloride staining, constituting 16.7+/-10.6% of the ipsilateral hemisphere. Good agreement of CBF functional maps with tissue specimens was found with respect to size and location of infarction. Best prediction of infarction was found for thresholds of CBF <10 mL/100 g per minute (mean size, 17.5+/-13.4%; r=0.95) and MTT >6 seconds (mean size, 15.6+/-13.5%; r=0.85), with regression slopes close to unity. CBV maps were less predictive of occurrence of infarction, especially in cases of small infarction. The short-term variability of CBF, CBV, and MTT in the control group was 10.9%, 15.2%, and 19.9%, respectively. CONCLUSIONS: Functional CT measurements of absolute CBF and MTT early after onset of ischemia allow prediction of the size and location of cerebral infarction with good accuracy.
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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".