The iScore Predicts Functional Outcome in Korean Patients With Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Several stroke risk scores for prediction of functional outcome have been developed, but rarely validated in Asian populations. We assessed the validity of the iScore, recently developed from Canadian stroke population, in an Asian stroke population from Korea. METHODS: We applied the iScore to 4061 eligible participants with acute ischemic stroke in the nationwide multicenter stroke registry in Korea. The main outcome was poor functional outcome defined as having a modified Rankin Scale 3 to 6 at 3 months after stroke onset. The secondary outcome was death at 3 months. C-statistics were calculated to assess performance of the iScore. RESULTS: Poor functional outcome was found in 1496 patients (36.8%), whereas death at 3 months occurred in 294 patients (7.2%). C-statistics were 0.819 (95% confidence interval, 0.805-0.833) for poor functional outcome and 0.861 (95% confidence interval, 0.840-0.883) for death. Overall, there was a high correlation between observed and expected outcomes for poor functional outcome (Pearson correlation coefficient, r=0.990) and for death (r=0.969) according to risk score. CONCLUSIONS: The iScore reliably predicts poor functional outcome or death at 3 months after stroke in Korean 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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".