Abstract T P33: Separating Acute Infarct Core From Penumbra Using Optimized Imaging And Standardized Post-processing In The Setting Of Ischemic Stroke: A CT Perfusion Study
Bibliographic record
Abstract
Background: In the setting of acute ischemic stroke, CT Perfusion (CTP) may change thrombolytic/endovascular treatment decisions when compared to NCCT/CTA alone. Two distinct thresholds may be useful in this regard: separation of 1) non-salvageable tissue (infarct core) from salvageable electrically silent tissue (penumbra), and 2) penumbra from benign oligemia (hypoperfused, but will survive if clot persists). Methods: CTP (120s,8cm) was performed on 180 patients within 12hrs of ischemic stroke. Two patient cohorts were analyzed: (1) recanalization (TICI 2b,3) 6s. For group (2) CBF, CBV, and Tmax values were obtained from within the final infarct region, and total ipsilateral hemisphere, excluding infarction. CBF, CBV and Tmax were used in univariate regression models. Results: For group (1)[n=11], mean time from CTP to recanalization was 60±19min. CBF parameter (thresholds for gray and white matter 7.2 and 5.2 ml•min-1•(100g)-1) had the highest sensitivity (90.9%) and specificity (81.8%) for infarction. For group (2)[n=15], the Tmax parameter (thresholds for gray and white matter 11.3s and 11.8s) had the highest sensitivity (86.6%) and specificity (80.0%) for penumbra. Conclusion: CTP thresholds derived from ultra-early reperfusers could potentially best define what is clinically dead in patients with acute ischemic stroke
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".