Intracerebral Hematoma Morphologic Appearance on Noncontrast Computed Tomography Predicts Significant Hematoma Expansion
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Hematoma expansion in intracerebral hemorrhage is associated with higher morbidity and mortality. The computed tomography (CT) angiographic spot sign is highly predictive of expansion, but other morphological features of intracerebral hemorrhage such as fluid levels, density heterogeneity, and margin irregularity may also predict expansion, particularly in centres where CT angiography is not readily available. METHODS: Baseline noncontrast CT scans from patients enrolled in the Predicting Hematoma Growth and Outcome in Intracerebral Hemorrhage Using Contrast Bolus CT (PREDICT) study were assessed for the presence of fluid levels and degree of density heterogeneity and margin irregularity using previously validated scales. Presence and grade of these metrics were correlated with the presence of hematoma expansion as defined by the PREDICT study on 24-hour follow-up scan. RESULTS: Three hundred eleven patients were included in the analysis. The presence of fluid levels and increasing heterogeneity and irregularity were associated with 24-hour hematoma expansion (P=0.021, 0.003 and 0.049, respectively) as well as increases in absolute hematoma size. Fluid levels had the highest positive predictive value (50%; 28%-71%), whereas margin irregularity had the highest negative predictive value (78%; 71%-85). Noncontrast metrics had comparable predictive values as spot sign for expansion when controlled for vitamin K, antiplatelet use, and baseline National Institutes of Health Stroke Scale, although in a combined area under the receiver-operating characteristic curve model, spot sign remained the most predictive. CONCLUSIONS: Fluid levels, density heterogeneity, and margin irregularity on noncontrast CT are associated with hematoma expansion at 24 hours. These markers may assist in prediction of outcomes in scenarios where CT angiography is not readily available and may be of future help in refining the predictive value of the CT angiography spot sign.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".