Predicting Intracerebral Hemorrhage Expansion With Noncontrast Computed Tomography
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although the computed tomographic angiography spot sign performs well as a biomarker for hematoma expansion (HE), computed tomographic angiography is not routinely performed in the emergency setting. We developed and validated a score to predict HE-based on noncontrast computed tomography (NCCT) findings in spontaneous acute intracerebral hemorrhage. METHODS: After developing the score in a single-center cohort of patients with intracerebral hemorrhage (n=344), we validated it in a large clinical trial population (n=954) and in a multicenter intracerebral hemorrhage cohort (n=241). The following NCCT markers of HE were analyzed: hypodensities, blend sign, hematoma shape and density, and fluid level. HE was defined as hematoma growth >6 mL or >33%. The score was created using the estimates from multivariable logistic regression after final predictors were selected from bootstrap samples. RESULTS: =0.0002) were predictors of HE. A 5-point score was created (BAT score: 1 point for blend sign, 2 points for any hypodensity, and 2 points for timing of NCCT <2.5 hours). The c statistic was 0.77 (95% CI, 0.70-0.83) in the development population, 0.65 (95% CI 0.61-0.68) and 0.70 (95% CI, 0.64-0.77) in the 2 validation cohorts. A dichotomized score (BAT score ≥3) predicted HE with 0.50 sensitivity and 0.89 specificity. CONCLUSIONS: An easy to use 5-point prediction score can identify subjects at high risk of HE with good specificity and accuracy. This tool requires just a baseline NCCT scan and may help select patients with intracerebral hemorrhage for antiexpansion clinical trials.
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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 | 0.000 | 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) | 0.001 | 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 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".