Totaled Health Risks in Vascular Events Score Predicts Clinical Outcome and Symptomatic Intracranial Hemorrhage in Chinese Patients After Thrombolysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The performance of the Totaled Health Risks in Vascular Events (THRIVE) score in predicting clinical outcomes in Chinese patients with acute ischemic stroke post intravenous thrombolysis is unknown. METHODS: Data from the Thrombolysis Implementation and Monitor of Acute Ischemic Stroke in China (TIMS-China) study was used to compare the THRIVE score with other scores used to predict clinical outcomes and symptomatic intracranial hemorrhage after intravenous thrombolysis. RESULTS: Among the 1128 patients with acute ischemic stroke who were included in this study, areas under the curve of the THRIVE score for symptomatic intracranial hemorrhage, 3-month poor functional outcomes, and death rate were 0.69, 0.71, and 0.78, respectively. The increased THRIVE score was related to the higher risk of developing symptomatic intracranial hemorrhage, poor functional outcomes, or death in patients with acute ischemic stroke at 3 months after thrombolysis. CONCLUSIONS: The THRIVE score predicted reliably the risks of developing symptomatic intracranial hemorrhage, poor functional outcome, or death after intravenous thrombolysis therapy in Chinese patients with acute ischemic stroke.
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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 | 0.000 |
| 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".