Predictors of severe intracerebral hemorrhage expansion
Bibliographic record
Abstract
BACKGROUND: Severe hematoma expansion (sHE) has the strongest impact on intracerebral hemorrhage (ICH) outcome. We investigated the predictors of sHE. METHODS: Retrospective analysis of ICH patients admitted at nine sites in Italy, Germany, China, and Canada. The following imaging features were analyzed: non-contrast CT (NCCT) hypodensities, heterogeneous density, blend sign, irregular shape, and CT angiography (CTA) spot sign. The outcome of interest was sHE, defined as volume increase >66% and/or >12.5 from baseline to follow-up NCCT. Predictors of sHE were explored with logistic regression. RESULTS: A total of 1472 patients were included (median age 73, 56.6% males) of whom 223 (15.2%) had sHE. Age (odds ratio (OR) per year, 95% confidence interval (CI), 1.02 (1.01-1.04)), Anticoagulant treatment (OR 3.00, 95% CI 2.09-4.31), Glasgow Coma Scale (OR 0.93, 95% CI 0.89-0.98), time from onset/last known well to imaging, (OR per h 0.96, 95% CI 0.93-0.99), and baseline ICH volume, (OR per mL 1.02, 95% CI 1.02-1.03) were independently associated with sHE. Ultra-early hematoma growth (baseline volume/baseline imaging time) was also a predictor of sHE (OR per mL/h 1.01, 95% CI 1.00-1.02). All NCCT and CTA imaging markers were also predictors of sHE. Amongst imaging features NCCT hypodensities had the highest sensitivity (0.79) whereas the CTA spot sign had the highest positive predictive value (0.51). CONCLUSIONS: sHE is common in the natural history of ICH and can be predicted with few clinical and imaging variables. These findings might inform clinical practice and future trials targeting active bleeding in ICH.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".