Predictors of intracranial hemorrhage after mechanical thrombectomy using a stent-retriever for anterior circulation ischemic stroke: A retrospective study
Bibliographic record
Abstract
Intracranial hemorrhage (ICH) after mechanical thrombectomy (MT) is a potentially catastrophic complication. We aimed to identify predictors of hemorrhagic complications following MT using a stent-retriever (SR) for acute ischemic stroke (AIS) patients due to large vessel occlusion of anterior circulation. In consecutive AIS patients, the clinical and procedural variables were retrospectively analyzed. ICH was evaluated on computed tomography performed 24 hours following MT and dichotomized into asymptomatic ICH and symptomatic intracranial hemorrhage (SICH) depending on the presence of neurological deterioration. Using univariate and multivariate analyses, the predictors of ICH and SICH were identified. The optimal cutoff value for predicting SICH was determined by receiver operating characteristic (ROC) analysis. Among 135 patients, ICH was detected in 52 (38.5%), and 17 (12.6%) were classified as having SICH. We found that serum glucose level (odds ratio [OR] 1.016, P = .011) and number of SR passes (OR 2.607, P < .001) were significantly correlated with ICH. Independent predictors of SICH included the baseline Alberta stroke program early computed tomography score (ASPECTS) (OR 0.485, P = .042), time from stroke onset to groin puncture (OTP) (OR 1.033, P = .016), and number of SR passes (OR 2.342, P = .038). In ROC analysis, baseline ASPECTS ≤ 7, OTP > 280 minutes, and SR passes > 3 were the optimal cutoff values for predicting SICH. In conclusion, serum glucose level and SR pass serve as predictors for any form of ICH in large vessel occlusion-induced AIS patients undergoing MT. Moreover, patients with lower ASPECTS, prolonged OTP, and multiple SR passes are more vulnerable to SICH.
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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 | 0.001 |
| 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".