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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".