CT Perfusion in Acute Stroke: “Black Holes” on Time‐to‐Peak Image Maps Indicate Unsalvageable Brain
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: CT perfusion is becoming important in acute stroke imaging to determine optimal patient-management strategies. The purpose of this study was to examine the predictive value of time-to-peak image maps and, specifically, a phenomenon coined a "black hole" for assessing infarcted brain tissue at the time of scan. METHODS: Acute stroke patients were screened for the presence of black holes and their follow-up imaging (noncontrast CT or MR) was reviewed to assess for infarcted brain tissue. RESULTS: Of the 23 patients with signs of acute ischemia on CT perfusion, all had black holes. The black holes corresponded with areas of infarcted brain on follow-up imaging (specificity 100%). Black holes demonstrated significantly lower cerebral blood volumes (P < .001) and cerebral blood flow (P < .001) compared to immediately adjacent tissue. CONCLUSIONS: Black holes on time-to-peak image maps represent areas of unsalvageable brain.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".