Abstract 68: Risk Score for Intracranial Hemorrhage in Patients with Acute Ischemic Stroke Treated With Intravenous t-PA.
Bibliographic record
Abstract
Background: From Get With The Guidelines-Stroke (GWTG-Stroke), we identified characteristics of patients with IV t-PA related symptomatic ICH (sICH) within 36 hours of stroke symptom onset and derived and validated a prediction tool for determining sICH risk. Methods: The population consisted of 10,242 patients from 988 hospitals that presented directly to the emergency department and received IV t-PA within 3 hours of symptom onset who were entered into the GWTG-Stroke registry (January 2009 - June 2010). This sample was randomly divided into derivation (70%) and validation (30%) cohorts. Multivariable logistic regression model identified predictors of IV t-PA related sICH in the derivation sample from candidate demographic, medical history, and laboratory variables collected at admission; model beta coefficients were used to assign point scores for prediction. Results: sICH within 36 hours was noted in 496 patients (4.8%). The multivariable adjusted independent predictors of increased risk of sICH post IV t-PA were increasing age (17 points), higher baseline NIHSS (42 pts), higher systolic BP (21 pts), higher blood glucose (8 pts), Asian race (9 pts, compared to all other races) and male gender (4 pts). In secondary analyses, we did not find an increased risk of sICH in patients taking warfarin when INR ≤ 1.7. The risk score demonstrated good discrimination (C-statistic 0.71) and excellent calibration as demonstrated by the Hosmer-Lemeshow statistic (P=0.48) and plot of predicted versus observed mortality in the whole cohort. ( Figure ) Conclusions: The GWTG-Stroke sICH risk score provides clinicians with a validated method to determine the risk of symptomatic ICH in patients treated with IV t-PA within 3 hours of stroke symptom onset. Figure: Observed vs Predicted % risk of sICH post IV t-PA in the derivation and validation cohort.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".