Serial changes in CT cerebral blood volume and flow after 4 hours of middle cerebral occlusion in an animal model of embolic cerebral ischemia.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Neuroimaging techniques have the potential to improve acute stroke treatment by selecting the appropriate patients for thrombolytic therapy. In this study, we examined changes in cerebral blood flow (CBF) and cerebral blood volume (CBV) in an animal model of middle cerebral artery occlusion and used these to identify the parameters that best differentiate between oligemic and infarct regions. MATERIALS AND METHODS: Permanent middle cerebral artery occlusion was performed in 17 New Zealand white rabbits. CT perfusion imaging was performed before (baseline), 10, and 30 minutes after the stroke, and then every 30 minutes up to 3 hours. After a final scan at 4 hours, the brain was removed, cut corresponding to CT sections, and stained with 2,3,5-triphenyltetrazolium chloride (TTC) to identify infarcted tissue. A logistic regression model with the 4-hour post-CBF and -CBV values as independent variables was used to determine the binary tissue outcome variable (oligemia or infarction). RESULTS: Infarcted regions were characterized by a significant decrease (P < .005) in both CBV and CBF, whereas oligemic (CBF < 25 mL . 100 g(-1) . min(-1), not infarcted) regions showed a significant decrease (P < .005) in CBF with maintenance of CBV at or near baseline values. From the perfusion parameters at the 4-hour time point, logistic regression by using CBV*CBF resulted in a sensitivity of 90.6% and a specificity of 93.3% for infarction. CONCLUSION: CBF and CBV values obtained from CT perfusion imaging can be used to distinguish between oligemic and infarct regions. This information could be used to assess the viability of ischemic brain tissue.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".