Clinical, laboratory, and radiographic predictors of the occurrence of seizures following aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
OBJECT: At present, the administration of prophylactic antiepileptic medication following aneurysmal subarachnoid hemorrhage (SAH) is controversial, and the practice is heterogeneous. Here, the authors sought to inform clinical decision making by identifying factors associated with the occurrence of seizures following aneurysm rupture. METHODS: Exploratory analysis was performed on 413 patients enrolled in CONSCIOUS-1 (Clazosentan to Overcome Neurological Ischemia and Infarction Occurring after Subarachnoid Hemorrhage), a prospective randomized trial of clazosentan for the prevention of angiographic vasospasm. The association among clinical, laboratory, and radiographic covariates and the occurrence of seizures following SAH were determined. Covariates with a significance level of p < 0.20 on univariate analysis were entered into a multivariate logistic regression model. Receiver operating characteristic (ROC) curve analysis was used to define optimal predictive thresholds. RESULTS: Of the 413 patients enrolled in the study, 57 (13.8%) had at least 1 seizure following SAH. On univariate analysis, a World Federation of Neurosurgical Societies grade of IV-V, a greater subarachnoid clot burden, and the presence of midline shift and subdural hematomas were associated with seizure activity. On multivariate analysis, only a subarachnoid clot burden (OR 2.76, 95% CI 1.39-5.49) and subdural hematoma (OR 5.67, 95% CI 1.56-20.57) were associated with seizures following SAH. Using ROC curve analysis, the optimal predictive cutoff for subarachnoid clot burden was determined to be 21 (of a possible 30) on the Hijdra scale (area under the curve 0.63). CONCLUSIONS: A greater subarachnoid clot burden and subdural hematoma are associated with the occurrence of seizures after aneurysm rupture. These findings may help to identify patients at greatest risk for seizures and guide informed decisions regarding the prescription of prophylactic anticonvulsive therapy. Clinical trial registration no.: NCT00111085 (ClinicalTrials.gov).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".