Abstract 116: Predicting Symptomatic Intracranial Hemorrhage After Mechanical Thrombectomy: The TAG Score
Bibliographic record
Abstract
Background: There is limited data on predictors of sICH in patients who underwent mechanical thrombectomy. In this study, we aim to determine those predictors with external validation. Methods: We evaluated mechanical thrombectomy in a derivation cohort of patients at a comprehensive stroke center over a 30-month period. sICH was defined using the European Cooperative Acute Stroke Study III. We compared clinical and radiographic characteristics between patients with and without sICH to identify independent predictors of sICH with p<0.1. We then derived an sICH prediction score and validated it using the Blood Pressure After Endovascular Treatment (BEST) multicenter prospective registry. Results: We identified 578 patients with acute ischemic stroke who received thrombectomy, 19 had sICH (3.3%). Predictive factors of sICH were: Thrombolysis in cerebral ischemia score, Alberta stroke program early computed tomography score (ASPECTS), and Glucose level, and using these predictors, we derived the weighted TAG score which was associated with sICH in the derivation (OR per unit increase 1.98, 95% CI 1.48-2.66, AUC=0.79) and validation (OR per unit increase 1.48, 95% CI 1.22-1.79, AUC=0.69) cohorts. Conclusion: High TAG scores are associated with sICH in patients receiving mechanical thrombectomy. Larger studies are needed to validate this scoring system and test strategies to reduce sICH risk and make thrombectomy safer in patients with elevated TAG scores.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 0.001 |
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".