Symptomatic Intracranial Hemorrhage After Mechanical Thrombectomy in Chinese Ischemic Stroke Patients
Bibliographic record
Abstract
Background and Purpose: Symptomatic intracranial hemorrhage (sICH), potentially associated with poor prognosis, is a major complication of endovascular thrombectomy (EVT) for ischemic stroke patients. We aimed to develop and validate a risk model for predicting sICH after EVT in Chinese patients due to large-artery occlusions in the anterior circulation. Methods: The derivation cohort recruited patients with EVT from the Endovascular Treatment for Acute Anterior Circulation Ischemic Stroke Registry in China. sICH was diagnosed according to the Heidelberg Bleeding Classification within 24 hours of EVT. Stepwise logistic regression was performed to derive the predictive model. The discrimination and calibration of the risk model were assessed using the C index and the calibration plot. An additional cohort of 503 patients from 2 stroke centers was prospectively enrolled to validate the new model. Results: We enrolled 629 patients who underwent EVT as the derivation cohort, among whom 87 developed sICH (13.8%). In the multivariate adjustment, Alberta Stroke Program Early CT Score (odds ratio [OR], 0.85; P =0.005), baseline glucose (OR, 1.13; P =0.001), poor collateral circulation (OR, 3.06; P =0.001), passes with retriever (OR, 1.52; P =0.001), and onset-to-groin puncture time (OR, 1.79; P =0.024) were independent factors of sICH and were incorporated as the Alberta Stroke Program Early CT Score, Baseline Glucose, Poor Collateral Circulation, Passes With Retriever, and Onset-to-Groin Puncture Time (ASIAN) score. The ASIAN score demonstrated good discrimination in the derivation cohort (C index, 0.771 [95% CI, 0.716–0.826]), as well as the validation cohort (C index, 0.758 [95% CI, 0.691–0.825]). Conclusions: The ASIAN score reliably predicts the risk of sICH in Chinese ischemic stroke patients treated by 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".