MRI of ischemic stroke in canines: Applications for monitoring intraarterial thrombolysis
Bibliographic record
Abstract
PURPOSE: To describe a canine embolic stroke model that is appropriate for endovascular procedure evaluations and develop local cerebral blood flow (CBF) maps to monitor the progression of stroke and thrombolysis. In the future, MR may displace X-ray imaging in some endovascular procedures, such as intraarterial (IA) thrombolysis for stroke therapy, due to increased monitoring capabilities. For MR to attain its full potential in endovascular therapy, the development of appropriate disease models and monitoring techniques is essential. MATERIALS AND METHODS: The canine stroke model uses an injection of autologous clot to produce ischemic and infarcted tissue and produces a range of stroke severities within the anterior cerebral circulation. Local CBF maps were formed by using the catheter that would be in place to deliver the thrombolytic agent for treatment to deliver the gadolinium-based contrast agent for perfusion imaging. RESULTS: After the injection of clot, changes on imaging were consistent with the progression of ischemic stroke. Local CBF maps showed perfusion changes with stroke progression and treatment. CONCLUSION: We successfully demonstrate the progression of ischemic stroke in the canine to mimic the progression of human stroke. CBF maps to show local perfusion characteristics show great potential in the evaluation of stroke therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".