Abstract TP44: Low Cerebral Blood Flow (CBF) at Baseline Best Predicts Parenchymal Hematoma (PH) Post Revascularization Therapy
Bibliographic record
Abstract
Introduction: In patients with acute ischemic stroke (AIS), parenchymal hematoma (PH) after revascularization therapy can lead to clinical deterioration and is often unpredictable. We sought to examine the association between admission CT perfusion (CTP) parameters (cerebral blood volume (CBV), cerebral blood flow (CBF), and Tmax) and PH in a case-controlled sample of patients receiving intravenous tPA ± endovascular treatment when using an industry standard CTP algorithm. Hypothesis: We hypothesize that CTP derived parameters (CBV, CBF, and Tmax) can predict PH in AIS. Methods: PH was classified according to ECASS II criteria. Two-phase (150s) and single-phase (66s) CTP acquisitions were performed within 12hrs of ictus. CTP 4D(GE Healthcare) delay-insensitive software was used to calculate CBF, CBV, and Tmax maps. Ipsilateral hemisphere gray and white matter (GM, WM) were flooded to determine volumes (mm3) for 3 lesion types: 1) patient-specific very low CBV (vlCBV) thresholds derived from the lower 10th, 5th and 2.5th percentiles of the contralateral hemisphere, 2) very low CBF threshold of ≤7ml/(min·100g), and 3) very high Tmax threshold of ≥16s. To correct for varying scan coverage, ratio of threshold output volume by ipsilateral hemisphere volume within slices that contained lesions was obtained. Receiver operating characteristic (ROC) analysis was used to compare models to determine which CTP parameter best predicted PH. Results: 34 AIS patients (18 PH, 16 no hemorrhage) were included. Very low CBF threshold of ≤7ml/(min·100g) best predicted the occurrence of PH post revascularization therapy (p < 0.001; comparison of c-statistic). CBV and Tmax parameters were not discriminative of PH. (See Figure 1 for comparison of c- statistics). Conclusion: When using a delay insensitive industry standard CTP paradigm, very low CBF≤7ml/(min·100g) has the ability in predicting PH post revascularization therapy.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".