External Validation of the iScore for Predicting Ischemic Stroke Mortality in Patients in China
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The iScore is a prediction tool developed to estimate the risk of death in patients after hospitalization for an acute ischemic stroke. Our aim was to determine the accuracy of the iScore in patients with ischemic stroke in China. METHODS: The iScore was used to predict 30-day mortality rate in 11 656 patients and 1-year mortality rate in 11 051 patients with acute ischemic stroke. These patients were identified from the China National Stroke Registry (CNSR) data set. Model discrimination was quantified by calculating the C statistic. The calibration was assessed using Pearson correlation coefficient. RESULTS: The 30-day and 1-year mortality rates were 5.4% and 14.3%, respectively. The C statistics were 0.825 (95% confidence interval, 0.807-0.843) for 30-day mortality and 0.822 (95% confidence interval, 0.810-0.833) for 1-year mortality. The plots of observed versus predicted mortality rates showed excellent model calibration in the external validation samples from the CNSR (Pearson correlation coefficient, 0.925 for 30-day and 0.998 for 1-year mortality; both P<0.0001). CONCLUSIONS: The iScore reliably predicts 30-day and 1-year mortality in Chinese patients with 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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".