THRIVE Score Predicts Ischemic Stroke Outcomes and Thrombolytic Hemorrhage Risk in VISTA
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In previous studies, the Totaled Health Risks in Vascular Events (THRIVE) score has shown broad utility, allowing prediction of clinical outcome, death, and risk of hemorrhage after tissue-type plasminogen activator (tPA) treatment, irrespective of the type of acute stroke therapy applied to the patient. METHODS: We used data from the Virtual International Stroke Trials Archive to further validate the THRIVE score in a large cohort of patients receiving tPA or no acute treatment, to confirm the relationship between THRIVE and hemorrhage after tPA, and to compare the THRIVE score with several other available outcome prediction scores. RESULTS: The THRIVE score strongly predicts clinical outcome (odds ratio, 0.55 for good outcome [95% CI, 0.53-0.57]; P<0.001), mortality (odds ratio, 1.57 [95% confidence interval, 1.50-1.64]; P<0.001), and risk of intracerebral hemorrhage after tPA (odds ratio, 1.34 [95% confidence interval, 1.22-1.46]; P<0.001). The relationship between THRIVE score and outcome is not influenced by the independent relationship of tPA administration and outcome. In receiver operator characteristic curve analysis, the THRIVE score was superior to several other available outcome prediction scores in the prediction of clinical outcome and mortality. CONCLUSIONS: The THRIVE score is a simple-to-use tool to predict clinical outcome, mortality, and risk of hemorrhage after thrombolysis in patients with ischemic stroke. Despite its simplicity, the THRIVE score performs better than several other outcome prediction tools. A free Web calculator for the THRIVE score is available at http://www.thrivescore.org.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".