Abstract 191: Development and Validation of a Novel Risk Score for Assessing Risk of In-hospital Seizure Following Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
Introduction: A simple tool that could support individualized approach to anticonvulsant prophylaxis during hospital admission in patients with subarachnoid hemorrhage (SAH) would have clinical utility. This study was aimed at developing such a tool. Methods: We developed a risk score in 1500 patients from the SAH outcomes project of Columbia University, and validated it in 825 patients derived from the Washington University Database of SAH including patients from the CONSCIOUS 1 trial. Candidate predictors were identified by systematic review of literature and included in a backward stepwise logistic regression model with in-hospital seizure as dependent variable. The performance of the risk score was assessed using the area under the receiver operator characteristics curve (AUC) and calibration plots. Results: The SAFARI score (Seizure AFter aneurysmal subarachnoid hemorrhage RIsk score) based on four items including age > 60 years, seizure occurrence prior to hospitalization, aneurysm location and hydrocephalus had AUC = 0.77 (95% Confidence Intervals [CI]: 0.73 – 0.82) in the development cohort. In the validation cohort, the AUC was 0.65 (95% CI: 0.56 – 0.73). Calibration plots demonstrated excellent agreement between observed and predicted risk of in-hospital seizure. Conclusions: We have developed a simple tool for predicting the likelihood of seizure during hospitalization for SAH. The SAFARI score could be helpful to guide treatment choices regarding the use of anticonvulsants during admission, and to optimize patient enrolment for clinical trials evaluating seizure management after SAH, among other benefits.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".