Predictive Value of RAPID Assessed Perfusion Thresholds on Final Infarct Volume in SWIFT PRIME (Solitaire With the Intention for Thrombectomy as Primary Endovascular Treatment)
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Computed tomography perfusion imaging can estimate the size of the ischemic core, which can be used for the selection of patients for endovascular therapy. The relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) thresholds chosen to identify ischemic core influence the accuracy of prediction. We aimed to analyze the accuracy of various rCBV and rCBF thresholds for predicting the 27-hour infarct volume using RAPID automated analysis software from the SWIFT PRIME trial (Solitaire With the Intention for Thrombectomy as Primary Endovascular Treatment) data. METHODS: Patients from the SWIFT PRIME study who achieved complete reperfusion based on time until the residue function reached its peak >6 s perfusion maps obtained at 27 hours were included. Patients from both the intravenous tissue-type plasminogen activator only and endovascular groups were included in analysis. Final infarct volume was determined on magnetic resonance imaging (fluid-attenuated inversion recovery images) or computed tomography scans obtained 27 hours after symptom onset. The predicted ischemic core volumes on rCBV and rCBF maps using thresholds ranging between 0.2 and 0.8 were compared with the actual infarct volume to determine the most accurate thresholds. RESULTS: Among the 47 subjects, the following baseline computed tomography perfusion thresholds most accurately predicted the actual 27-hour infarct volume: rCBV=0.32, median absolute error (MAE)=9 mL; rCBV=0.34, MAE=9 mL; rCBF=0.30, MAE=8.8 mL; rCBF=0.32, MAE=7 mL; and rCBF=0.34, MAE=7.3. CONCLUSIONS: Brain regions with rCBF 0.30 to 0.34 or rCBV 0.32 to 0.34 thresholds provided the most accurate prediction of infarct volume in patients who achieved complete reperfusion with MAEs of ≤9 mL. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01657461.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".