Abstract WMP89: CT Hypodensities in Acute Intracerebral Hemorrhage Predict Hematoma Expansion
Bibliographic record
Abstract
Introduction: Hematoma expansion (HE) is a potentially modifiable predictor of poor outcome following acute intracerebral hemorrhage (ICH) and a common therapeutic target in recent and ongoing clinical trials. Our ability to identify ICH patients likeliest to expand and therefore likeliest to benefit from HE-targeted treatments remains an unmet need. Hypodensities (hDs) within ICH on noncontrast CT (NCCT), sometimes referred to as “swirl sign,” have been suggested as a possible predictor of HE. We sought to determine whether hDs can predict HE independently of other known predictors of HE. Methods: hDs were analyzed by 2 independent blinded raters in consecutive ICH patients with baseline CT/CT Angiography (CTA) and follow-up NCCTs. The association of hDs and HE (>6cc or 33% of baseline volume) was determined by multivariable logistic regression controlling for other variables (including the CTA spot sign) associated with HE in univariate analyses with p≤ 0.1. Results: Among 414 patients, 114 (27.5%) baseline NCCTs demonstrated hDs (Figure, white arrowheads; kappa for interrater reliability 0.87). In univariate analyses, hDs were strongly associated with HE (40.3% hDs in patients with HE, 10.3% in patients without, p<0.0001) and with warfarin use, shorter time to CT, larger baseline ICH volume, CTA spot sign presence, and older age. The association between hDs and HE remained significant (OR = 2.89 [95%CI 1.5-5.6], p=0.001) in a multivariable model controlling for the above factors and sex; other independent predictors of HE were CTA spot sign, shorter time to CT and older age (all p<.001). Discussion: hDs within an acute ICH on NCCT are a reliable and strong predictor of HE, independent of other clinical and imaging predictors. This novel marker may help clarify the mechanism of HE and serve as a useful addition to clinical algorithms for determining HE risk.
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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 | 0.008 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".