Hyperperfusion on perfusion computed tomography following revascularization for acute stroke
Bibliographic record
Abstract
PURPOSE: To describe the findings of hyperperfusion on perfusion computed tomography (CT) in four patients following revascularization for acute stroke. MATERIAL AND METHODS: In 2002-2003, among a series of 6 patients presenting with an acute stroke and treated with intra-arterial thrombolysis, we observed the presence of hyperperfusion in 3 patients on the follow-up CT perfusion. We included an additional patient who was treated with intravenous thrombolysis and who had hyperperfusion on the follow-up CT perfusion. We retrospectively analyzed their CT perfusion maps. Cerebral blood volume (CBV) and cerebral blood flow (CBF) maps were compared between the affected territory and the normal contralateral hemisphere. RESULTS: In the four patients, the mean CBV and CBF were 3.6 +/- 2.0 ml/100 g and 39 +/- 25 ml/100 g/min in the affected territory compared to the normal side (mean CBV = 2.7 +/- 2.1 ml/100 g, mean CBF = 27 +/- 23 ml/100 g/min). There was no intracranial hemorrhage in the hyperperfused territories. At follow-up CT, some hyperperfused brain areas progressed to infarction, while others retained normal white to gray matter differentiation. CONCLUSION: CT perfusion can demonstrate hyperperfusion, which can be seen in an ischemic brain territory following recanalization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".