Abstract WMP13: Predictive Value of Rapid-assessed Cerebral Blood Volume and Cerebral Blood Flow Ct Perfusion Thresholds on Final Infarct Volume Following Successful Reperfusion: Analysis of The SWIFT PRIME Trial Data
Bibliographic record
Abstract
Background and purpose: Advanced CT perfusion (CTP) imaging can estimate the size of the ischemic core, which can be used for selection of patients for endovascular therapy. The relative cerebral blood volume (rCBV) and cerebral blood flow (rCBF) thresholds chosen to identify ischemic core influence the accuracy of prediction. The purpose of this study was to analyze the accuracy of various rCBV and rCBF thresholds for predicting 27 hour infarct volume using RAPID automated analysis software. Methods: Patients from the SWIFT PRIME study with baseline and 27 hour follow-up CT perfusion scans were included if they had complete reperfusion based on Tmax>6s perfusion maps obtained at 27 hours. Patients from both the tPA and endovascular groups were included. Infarct volume was determined on MRI (FLAIR images) or CT scans obtained 27 hours after symptom onset. The predicted ischemic core volume on rCBV and rCBF maps using thresholds ranging between 0.2 and 0.8 was compared with the actual infarct volume to determine the most accurate thresholds. Results: Among the 47 subjects, the following baseline CTP thresholds most accurately predicted the actual 27 hour infarct volume: rCBV=0.34 (Median absolute error (MAE)=11.5 ml); rCBV=0.36, MAE=9.9 ml; rCBV=0.4, MAE=12.5 ml; rCBF=0.3, MAE=8.8 ml; rCBF=0.32, MAE=7.3; and rCBF=0.34, MAE=7.8. Conclusions: Brain regions with rCBF ≤ .32 or rCBV ≤ .36 provided the most accurate prediction of infarct volume in patients who achieved complete reperfusion with median absolute errors less than 10 ml. Our data support the value of automated image analysis software as a tool for accurate prediction of ischemic core lesion volume.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".