Clinical and imaging predictors for hemorrhagic transformation of acute ischemic stroke after endovascular thrombectomy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Hemorrhagic transformation (HT) is a common complication of endovascular thrombectomy (EVT) in patients with acute ischemic stroke (AIS). Our study aims to investigate the clinical and imaging predictors of HT and symptomatic intracranial hemorrhage (sICH) in patients who underwent EVT. METHODS: A retrospective analysis of 118 patients undergoing EVT for acute anterior circulation stroke was performed. Potential clinical and imaging predictors of all patients were collected and multivariate logistic regression was performed. The risk prediction system was constructed according to the multivariate logistic regression results. RESULTS: The incidence of HT and sICH after EVT were 46.6% and 15.3%, respectively. The multivariate logistic regression results showed that Alberta Stroke Program Early CT Score (ASPECTS) (p = .001, odds ratio [OR] = 0.367, 95% [confidence interval] CI, 0.201-0.670), collateral status (p<.001, OR = 0.117, 95% CI, 0.042-0.325), relative cerebral blood flow (CBF) ratio (p = .025, OR = 0.943, 95% CI, 0.895-0.993), and blood glucose on admission (p = .012, OR = 1.258, 95% CI, 1.053-1.504) were associated with HT. While for sICH, collateral circulation (p = .007, OR = 0.148, 95% CI, 0.037-0.589), ASPECTS (p = .033, OR = 0.510, 95% CI, 0.274-0.946), and blood glucose (p = .005, OR = 1.304, 95% CI, 1.082-1.573) were independent factors. The predictive model for HT after EVT was established, and the sensitivity and specificity of it were 90.9% and 79.4%, respectively, with the area under the curve of 90.0% (84.5%-95.4%). CONCLUSION: Collateral status, ASPECTS, relative CBF ratio, and blood glucose on admission were predictors for HT in AIS patients, while collateral status, ASPECTS, and blood glucose on admission were also predictors for sICH. In addition, the established predictive model showed good diagnostic value for prediction of HT after EVT.
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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 | 0.000 | 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 | 0.000 | 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".