Infarct core growth rate and 90-day outcomes in ischemic stroke: subgroup analysis based on onset-to-recanalization time
Bibliographic record
Abstract
Background It is essential to understand the factors that influence patient outcomes in stroke research. The infarct core growth rate (ICGR) is emerging as a potentially valuable marker, but its relationship with patient outcomes, especially concerning the onset-to-recanalization time (ORT), requires further clarification. This study investigates the impact of ICGR on 90-day (90d) outcomes in acute ischemic stroke patients and explores whether stratifying ICGR analysis based on ORT provides more detailed prognostic insights. Methods This study retrospectively analyzed patients with acute ischemic stroke with anterior circulation large vessel occlusion (AIS-ACLVO) who underwent endovascular treatment (EVT) between January 2021 and December 2023. Their clinical characteristics, baseline and imaging data were recorded upon admission. Clinical outcomes were evaluated using the modified Rankin Scale (mRS) at 90 days post-procedure. The least absolute shrinkage and selection operator (LASSO) regression was employed for data screening. Multivariable logistic regression analysis was performed to explore the relationship between ICGR and 90-day (90d) clinical outcome. Additionally, a stratified analysis based on ORT was conducted to compare the diagnostic performance of ICGR and infarct core volume (ICV) at different time points. Results A total of 153 patients were included in the analysis. Univariate and Lasso regression analyses showed that the group with unfavorable outcomes had statistically significant differences in ICGR, age, history of atrial fibrillation, history of drinking, admission blood glucose level, Alberta Stroke Program Early CT Score (ASPECTS), and National Institutes of Health Stroke Scale (NIHSS) score compared to the favorable outcome group (all p < 0.05). Furthermore, multivariate logistic regression analysis indicated that ICGR was independently associated with clinical outcome in AIS-ACLVO patients [Odds Ratio (OR) 1.101, 95% confidence interval (CI) 1.029–1.178; p = 0.005]. When stratified by median ORT, the ICGR remained a strong predictor of outcome within 8 h (OR 1.188, 95% CI 1.048–1.347; p = 0.007), and proved to be a better predictor than ICV [area under the Receiver Operating Characteristic (AUROC) curve, 0.816 vs. 0.750, p = 0.024]. Conclusion Our research indicates that the ICGR correlates with 90d clinical outcomes in AIS-ACLVO patients: a faster rate is associated with poorer outcomes. Within 8 h of ORT, the ICGR serves as a better predictor of 90d outcome than ICV.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".