Admission CT perfusion may overestimate initial infarct core: the ghost infarct core concept
Bibliographic record
Abstract
BACKGROUND: Identifying infarct core on admission is essential to establish the amount of salvageable tissue and indicate reperfusion therapies. Infarct core is established on CT perfusion (CTP) as the severely hypoperfused area, however the correlation between hypoperfusion and infarct core may be time-dependent as it is not a direct indicator of tissue damage. This study aims to characterize those cases in which the admission core lesion on CTP does not reflect an infarct on follow-up imaging. METHODS: We studied patients with cerebral large vessel occlusion who underwent CTP on admission but received endovascular thrombectomy based on a non-contrast CT Alberta Stroke Program Early CT Score (ASPECTS) >6. Admission infarct core was measured on initial cerebral blood volume (CBV) CTP and final infarct on follow-up CT. We defined ghost infarct core (GIC) as initial core minus final infarct >10 mL. RESULTS: 79 patients were studied. Median National Institutes of Health Stroke Scale (NIHSS) score was 17 (11-20), median time from symptoms to CTP was 215 (87-327) min, and recanalization rate (TICI 2b-3) was 77%. Thirty patients (38%) presented with a GIC >10 mL. GIC >10 mL was associated with recanalization (TICI 2b-3: 90% vs 68%; p=0.026), admission glycemia (<185 mg/dL: 42% vs 0%; p=0.028), and time to CTP (<185 min: 51% vs >185 min: 26%; p=0.033). An adjusted logistic regression model identified time from symptom to CTP imaging <185 min as the only predictor of GIC >10 mL (OR 2.89, 95% CI 1.04 to 8.09). At 24 hours, clinical improvement was more frequent in patients with GIC >10 mL (66.6% vs 39%; p=0.017). CONCLUSIONS: CT perfusion may overestimate final infarct core, especially in the early time window. Selecting patients for reperfusion therapies based on the CTP mismatch concept may deny treatment to patients who might still benefit from reperfusion.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".