Prognosis and Predictors of Symptomatic Intracranial Hemorrhage After Endovascular Treatment of Large Vessel Occlusion Stroke
Bibliographic record
Abstract
BACKGROUND: Symptomatic intracranial hemorrhage (sICH) is a devastating complication of endovascular treatment (EVT) in patients with acute ischemic stroke (AIS) and is associated with high risk of disability and mortality. This study intended to evaluate the predictors of sICH after EVT in patients with large vessel occlusion (LVO)-induced AIS. METHODS: We conducted a retrospective review on consecutive AIS patients who underwent EVT in our University hospital between January 2019 and August 2020. The patients were classified into two groups based upon the occurrence of sICH. The main outcomes were the occurrence of sICH using the Heidelberg Bleeding Classification and functional condition at 90 days. Multivariate logistic regression analysis and receiver operating characteristics (ROC) curves were used to identify independent predictors of sICH after EVT. RESULTS: < 0.001), with the overall mortality being 112 (30.4%) at 90 days post- EVT. Results from univariate analysis showed significant differences between the two groups in the prevalence of diabetes, initial Alberta Stroke Program Early CT Score (ASPECTS) score, National Institutes of Health Stroke Scale (NIHSS) score after operation, the levels of fasting blood glucose (FBG), neutrophil to lymphocyte ratio (NLR), platelets (PLT), and thrombin time (TT) at admission. Multivariate logistic regression analysis showed that FBG ≥ 7.54 mmol/L (OR: 2.765; 95% confidence interval [CI]: 1.513-5.054), NLR ≥ 5.48 (OR: 2.711; 95% CI: 1.433-5.128), TT at admission ≥ 16.25 s (OR: 2.022; 95% CI: 1.115-3.667), and NIHSS score within 24 h after the operation ≥ 10 (OR: 3.728; 95% CI: 1.516-9.170) were independent predictors of sICH. The combination of NLR ≥ 5.48, FBG ≥ 7.54 mmol/L, TT at admission ≥ 16.25 s, and NIHSS score within 24 h after the operation ≥ 10 generated an optimal prediction model (AUC: 0.723). CONCLUSION: Higher levels of FDG, NLR, TT at admission, and NIHSS score after operation were associated with sICH after EVT in patients with LVO-induced AIS.
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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 | 0.004 |
| 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 | 0.000 | 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".