Association Between Hematoma Expansion Severity and Outcome and Its Interaction With Baseline Intracerebral Hemorrhage Volume
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hematoma expansion (HE) is a major determinant of neurologic deterioration and poor outcome in intracerebral hemorrhage (ICH) and represents an appealing therapeutic target. We analyzed the prognostic effect of different degrees of HE. METHODS: This was a retrospective analysis of patients with ICH admitted at 8 academic institutions in Italy, Germany, Canada, China, and the United States. All patients underwent baseline and follow-up imaging for HE assessment. Relative HE (rHE) was classified as follows: none (<0%), mild (0%-33%), moderate (33.1%-66%), and severe (>66%). Absolute HE (aHE) was classified as none (<0 mL), mild (0-6.0 mL), moderate (6.1-12.5 mL), and severe (>12.5 mL). Predictors of poor functional outcome (90 days modified Rankin Scale 4-6) were explored with logistic regression. RESULTS: = 0.002) while there was no association between mild or moderate HE and poor outcome. The association between severe HE and poor outcome was significant only in patients with baseline ICH volume below 30 mL. DISCUSSION: The strongest association between HE and outcome was observed in patients with smaller initial volume experiencing severe HE. These findings may inform clinical trial design and guide clinicians in selecting patients for antiexpansion therapies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".