Incorporating a Modified Graeb Score to the Modified Fisher Scale for Improved Risk Prediction of Delayed Cerebral Ischemia Following Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
BACKGROUND: Delayed cerebral ischemia (DCI) is a cause of poor outcome after aneurysmal subarachnoid hemorrhage. Risk scales to predict DCI have scarcely been evaluated for predictive accuracy. Accounting for volume of intraventricular hemorrhage (IVH) in the modified Fischer scale (mFS) may improve its predictive accuracy. OBJECTIVE: To compare the modified Graeb score (mGS) to the mFS for risk prediction of DCI, and to investigate whether incorporating an mGS cut-point into the mFS could improve predictive accuracy. METHODS: This retrospective analysis was based on the Clazosentan to Overcome Neurological Ischemia and Infarction Occurring after Subarachnoid Hemorrhage (CONSCIOUS-1) trial cohort. IVH volume was quantified with the mGS. The relation of the mGS to DCI was evaluated using logistic regression and the area under the receiver operator characteristics curve (AUC). An optimized mGS cut-point was identified using the Youden index, and was incorporated into the mFS to dichotomize grades 2 and 4. The AUC was used to compare the predictive performance of the mGS with that of the mFS, and to assess whether there was an improvement in DCI prediction after creation of the dichotomized scale. RESULTS: The mFS and the mGS had similar discrimination for DCI (AUC: 0.608 vs 0.618; P = .79). A new scale including both the mFS and mGS significantly improved the AUC compared to the mFS (AUC: 0.647 vs 0.608; P = .022). CONCLUSION: The mFS and the mGS have similar prognostic utility. Accounting for IVH volume improved prediction of DCI by the mFS.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".