Clinical and Imaging Parameters Associated With Hyperacute Infarction Growth in Large Vessel Occlusion Stroke
Bibliographic record
Abstract
Background and Purpose— Large vessel occlusion stroke leads to highly variable hyperacute infarction growth. Our aim was to identify clinical and imaging parameters associated with hyperacute infarction growth in patients with an large vessel occlusion stroke of the anterior circulation. Methods— Seven hundred twenty-two consecutive patients with acute stroke were prospectively included in our monocentric stroke registry between 2009 and 2017. We selected all patients with a large vessel occlusion stroke of the anterior circulation, documented times from symptom onset, and CT perfusion on admission for our analysis (N=178). Ischemic core volume was determined with CT perfusion using automated thresholds. Hyperacute infarction growth was defined as ischemic core volume divided by times from symptom onset, assuming linear progression during times from symptom onset to imaging on admission. For collateral assessment, the regional leptomeningeal collateral score (rLMC) was used. Clinical data included the National Institutes of Health Stroke Scale score on admission and cardiovascular risk factors. Regression analysis was performed to adjust for confounders. Results— Median ischemic core volume was 34.4 mL, and median hyperacute infarction growth was 0.27 mL/min. In regression analysis including age, sex, National Institutes of Health Stroke Scale, clot burden score, diabetes mellitus, smoking, hypercholesteremia, hypertension, Alberta Stroke Program Early CT Score, and rLMC scores, only the rLMC score had a significant, independent association with hyperacute infarction growth (adjusted β=−0.35; P <0.001). Trichotomizing patients by rLMC scores yielded 65 patients with good (rLMC >15), 67 with intermediate (rLMC 11–15) and 46 with poor collaterals (rLMC <11) with an infarction growth of 0.17 mL/min, 0.26 mL/min, and 0.41 mL/min, respectively. Conclusions— Hyperacute infarction growth strongly depends on collaterals. In primary stroke centers, hyperacute infarction growth may be extrapolated to estimate the stroke progression during transfer times to thrombectomy centers and to support decisions on which patients to transfer.
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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 | 0.000 |
| 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".