Ischemic core and hypoperfusion volumes predict infarct size in <scp>SWIFT PRIME</scp>
Bibliographic record
Abstract
OBJECTIVE: Within the context of a prospective randomized trial (SWIFT PRIME), we assessed whether early imaging of stroke patients, primarily with computed tomography (CT) perfusion, can estimate the size of the irreversibly injured ischemic core and the volume of critically hypoperfused tissue. We also evaluated the accuracy of ischemic core and hypoperfusion volumes for predicting infarct volume in patients with the target mismatch profile. METHODS: Baseline ischemic core and hypoperfusion volumes were assessed prior to randomized treatment with intravenous (IV) tissue plasminogen activator (tPA) alone versus IV tPA + endovascular therapy (Solitaire stent-retriever) using RAPID automated postprocessing software. Reperfusion was assessed with angiographic Thrombolysis in Cerebral Infarction scores at the end of the procedure (endovascular group) and Tmax > 6-second volumes at 27 hours (both groups). Infarct volume was assessed at 27 hours on noncontrast CT or magnetic resonance imaging (MRI). RESULTS: A total of 151 patients with baseline imaging with CT perfusion (79%) or multimodal MRI (21%) were included. The median baseline ischemic core volume was 6 ml (interquartile range= 0-16). Ischemic core volumes correlated with 27-hour infarct volumes in patients who achieved reperfusion (r = 0.58, p < 0.0001). In patients who did not reperfuse (<10% reperfusion), baseline Tmax > 6-second lesion volumes correlated with 27-hour infarct volume (r = 0.78, p = 0.005). In target mismatch patients, the union of baseline core and early follow-up Tmax > 6-second volume (ie, predicted infarct volume) correlated with the 27-hour infarct volume (r = 0.73, p < 0.0001); the median absolute difference between the observed and predicted volume was 13 ml. INTERPRETATION: Ischemic core and hypoperfusion volumes, obtained primarily from CT perfusion scans, predict 27-hour infarct volume in acute stroke patients who were treated with reperfusion therapies.
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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 | 0.002 |
| 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".