Intracranial hemorrhage prediction in acute ischemic stroke patients with anterior circulation tandem lesions following endovascular thrombectomy
Bibliographic record
Abstract
Background: Acute ischemic stroke (AIS) patients with anterior circulation tandem lesions (TL) face a heightened risk of hemorrhage following endovascular thrombectomy (EVT). Predictive models specifically for this complication in the TL population are currently lacking. Methods: This retrospective cohort study analyzed 200 AIS patients with anterior circulation TL who underwent EVT. Least Absolute Shrinkage and Selection Operator regression was used for feature selection. Multivariable logistic regression (LR) models predicting intracranial hemorrhage (ICH) and symptomatic intracranial hemorrhage (sICH) risk were developed and visualized as nomograms. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC). Results: After EVT, ICH occurred in 92 patients (46%) and sICH in 24 patients (12%). The LR model for ICH identified diabetes [odd ratio (OR) 2.454, 95% CI 1.137-5.297], drinking history (OR 2.230, 95% CI 1.160-4.288), and lower modified Thrombolysis in Cerebral Infarction (mTICI) score (OR 0.547, 95% CI 0.311-0.961) as significant independent predictors (AUC = 0.712). The LR model for sICH identified lower Glasgow Coma Scale (GCS) score (OR 0.820, 95% CI 0.695-0.968), lower mTICI score (OR 0.398, 95% CI 0.182-0.868), and lower Alberta Stroke Program Early CT Score (ASPECTS) (OR 0.795, 95% CI 0.641-0.984) as significant independent predictors (AUC = 0.830). Nomograms effectively quantified the contribution of predictors to outcome probabilities. Conclusion: In AIS patients with anterior circulation TL undergoing EVT, diabetes, drinking history, and lower mTICI score independently predict ICH risk, while lower GCS score, lower mTICI score, and lower ASPECTS independently predict sICH risk. The nomograms provide practical tools for individualized risk assessment, aiding clinical decision-making and perioperative management in this high-risk cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".