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 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".