External Validation of the ASTRAL Score to Predict 3- and 12-Month Functional Outcome in the China National Stroke Registry
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The ASTRAL score was recently introduced as a prognostic tool for acute ischemic stroke. It predicts 3-month outcome reliably in both the derivation and the validation European cohorts. We aimed to validate the ASTRAL score in a Chinese stroke population and moreover to explore its prognostic value to predict 12-month outcome. METHODS: We applied the ASTRAL score to acute ischemic stroke patients admitted to 132 study sites of the China National Stroke Registry. Unfavorable outcome was assessed as a modified Rankin Scale score >2 at 3 and 12 months. Areas under the curve were calculated to quantify the prognostic value. Calibration was assessed by comparing predicted and observed probability of unfavorable outcome using Pearson correlation coefficient. RESULTS: Among 3755 patients, 1473 (39.7%) had 3-month unfavorable outcome. Areas under the curve for 3 and 12 months were 0.82 and 0.81, respectively. There was high correlation between observed and expected probability of unfavorable 3- and 12-month outcome (Pearson correlation coefficient: 0.964 and 0.963, respectively). CONCLUSIONS: ASTRAL score is a reliable tool to predict unfavorable outcome at 3 and 12 months after acute ischemic stroke in the Chinese population. It is a useful tool that can be readily applied in clinical practice to risk-stratify acute stroke patients.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".