Relative cerebral blood volume is associated with collateral status and infarct growth in stroke patients in SWIFT PRIME
Bibliographic record
Abstract
We aimed to evaluate how predefined candidate cerebral perfusion parameters correlate with collateral circulation status and to assess their capacity to predict infarct growth in patients with acute ischemic stroke (AIS) eligible for endovascular therapy. Patients enrolled in the SWIFT PRIME trial with baseline computed tomography perfusion (CTP) scans were included. RAPID software was used to calculate mean relative cerebral blood volume (rCBV) in hypoperfused regions, and hypoperfusion index ratio (HIR). Blind assessments of collaterals were performed using CT angiography in the whole sample and cerebral angiogram in the endovascular group. Reperfusion was assessed on 27-h CTP; infarct volume was assessed on 27-h magnetic resonance imaging/CT scans. Logistic and rank linear regression models were conducted. We included 158 patients. High rCBV ( p = 0.03) and low HIR ( p = 0.03) were associated with good collaterals. A positive association was found between rCBV and better collateral grades on cerebral angiography ( p = 0.01). Baseline and 27-h follow-up CTP were available for 115 patients, of whom 74 (64%) achieved successful reperfusion. Lower rCBV predicted a higher infarct growth in successfully reperfused patients ( p = 0.038) and in the endovascular treatment group ( p = 0.049). Finally, rCBV and HIR may serve as markers of collateral circulation in AIS patients prior to endovascular therapy. CLINICAL TRIAL REGISTRATION: Unique identifier: NCT0165746.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".