Stroke Severity and Early Ischemic Changes Predict Infarct Growth Rate and Clinical Outcomes in Patients With Large‐Vessel Occlusion
Bibliographic record
Abstract
Background: The infarct growth rate (IGR) measures ischemic stroke progression and varies among patients. Clinicoradiological phenotypes of IGR are poorly understood. We evaluated the association of presentation stroke severity and early ischemic changes with infarct progression in patients who underwent successful thrombectomy. Methods: This is a retrospective cohort observational study of consecutive endovascular therapy patients with anterior circulation large-vessel occlusion strokes and successful reperfusion (modified Thrombolysis in Cerebral Ischemia≥2b) from 2 comprehensive stroke centers. National Institutes of Health Stroke Scale and Alberta Stroke Program Early CT [Computed Tomography] Score (ASPECTS) were scored at admission. IGR was defined as the final infarct volume after endovascular therapy divided by the time from stroke onset to successful reperfusion. We used the Youden J index to identify the optimal IGR cutoff to stratify fast and slow progressors. A multivariate logistic regression was used to identify variables associated with a fast IGR and clinical outcomes. Results: A total of 212 patients were included in the study. The optimal IGR threshold was 3.2 mL/h, and 135 patients (63.6%) were classified as fast progressors. Presentation National Institutes of Health Stroke Scale score (odds ratio [OR], 1.12; 95% CI, 1.06-1.19) and ASPECTS (OR, 0.56; 95% CI, 0.41-0.73) were accurate predictors of a fast IGR after adjusting for significant confounders. For each 1-point increase in National Institutes of Health Stroke Scale score at admission, the likelihood of being a fast progressor increased by 12%; for each 1-point increase in ASPECTS, the likelihood of being a fast progressor decreased by 44%. In the early window (≤6 hours), all patients with ASPECTS <7 were identified as fast progressors. Conclusions: This study shows that National Institutes of Health Stroke Scale score and ASPECTS at presentation could predict fast versus slow IGR in patients receiving endovascular therapy.
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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 | 0.001 |
| 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".