A Risk Score Based on Get With the Guidelines–Stroke Program Data Works in Patients With Acute Ischemic Stroke in China
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There are few validated models for prediction of in-hospital mortality after acute ischemic stroke. In 2010, Smith et al developed and internally validated models for predicting in-hospital mortality based on Get With the Guidelines-Stroke program data. We demonstrate the applicability of this Get With the Guidelines risk model in Chinese patients. METHODS: The prognostic model was used to predict survival in 7015 patients with acute ischemic stroke from China National Stroke Registry data set. Model discrimination was quantified by calculating C statistic. To clarify the role of National Institutes of Health Stroke Scale (NIHSS), we also calculated the C statistics for NIHSS alone and for the model without NIHSS. RESULTS: The C statistic was 0.867 (95% CI, 0.839-0.895) through the Get With the Guidelines risk model, suggesting good discrimination in the China National Stroke Registry. The model without NIHSS produced significantly lower C statistic (0.735; 95% CI, 0.701-0.770; P<0.001), indicating the important role of NIHSS in the prediction of survival. Furthermore, a model with NIHSS alone also provided significant discrimination (C statistic, 0.847; 95% CI, 0.816-0.879). A plot of observed versus predicted mortality showed excellent model calibration in the external validation sample from the China National Stroke Registry. CONCLUSIONS: The Get With the Guidelines risk model could correctly predict in-hospital mortality in Chinese patients with ischemic stroke. In addition, the NIHSS provides substantial incremental information on a patient's short-term mortality risk and is the strongest predictor of mortality.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".