Abstract WP372: The Acute ICH Growth Score: Simple and Accurate Predictor of Hematoma Expansion in Patients with Acute Intracerebral Hemorrhage
Bibliographic record
Abstract
Background and Purpose: Acute intracerebral hemorrhage (ICH) hematoma expansion predicts high mortality and morbidity, occurring in a third of patients presenting with this condition. Recent studies correlated ultra-early hematoma growth and hematoma morphologic appearance with ICH expansion. Our purpose was to develop simple and clinically useful score that would predict ICH hematoma expansion accurately. Methods: This cohort included patients with primary or anticoagulation-associated ICH patients presenting <6 hours post ictus prospectively enrolled in the PREDICT study. Patients underwent baseline CT, CT angiography and 24-hour CT for hematoma expansion analysis. A risk score model was developed for predicting hematoma expansion (> 6 ml or > 33%). A 7-point acute ICH growth score was based on ultra-early hematoma growth > 5 mL/hour (yes=1), irregular morphology (yes=1), density heterogeneity (yes=1), presence of fluid-blood levels (yes=1), spot sign (yes=1), and use of anticoagulation (yes=2). Discrimination of the expansion score was assessed. Results: We retrospectively studied 301 primary or anticoagulation-associated intracerebral hemorrhage patients. The 7-point acute ICH growth score demonstrated good discrimination for hematoma expansion>6 mL or 33% (area under the curve of 0.76). Median and significant HE are shown in the table below (p<0.001). Conclusions: In a multicenter prospective study, the ICH expansion score demonstrate good correlation with hematoma expansion, and included recently reported variables such as morphology and ultraearly growth.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".