Abstract 30: Association of Fluid Levels, Density Heterogeneity and Irregular Margins on Baseline Non-Contrast Computerized Tomography With Significant Hematoma Expansion in Intracerebral Hemorrhage
Bibliographic record
Abstract
Introduction: The PREDICT study confirmed that the spot sign was a valid predictor of expansion in intracerebral hemorrhage (ICH) at 24h, with a positive predictive value (PPV) of 61%. This technique requires access to computed tomography angiography (CTA). Other markers may enhance the ability to predict expansion in ICH, or be useful in cases where multimodal imaging is not available. Previous studies have suggested that markers on non-contrast computed tomography (NCCT) such as density heterogeneity within the hematoma, irregularity of external margins, or internal fluid levels are associated with hematoma expansion (HE). Methods: Baseline NCCT scans of patients enrolled in PREDICT were examined for the presence of internal fluid levels (defined as a change within the hematoma resulting in a linear interface between two discrete fluid densities) and for the presence of hematoma density heterogeneity and margin irregularity (using an ordinal scale of 1-5 defined, published and validated previously). The association of each marker with median 24h absolute growth and with significant HE (defined as an increase in hematoma size of 6 mL or 33% measured 24h from baseline) were determined. Results: Fluid levels were present in 29 (8.3%) of 351 eligible patients. The presence of fluid levels were associated with significant HE at 24h (χ2 =7.64, df=1, p<0.01) and with a trend toward increased median absolute ICH volume at 24h (6.4 mL vs. 0.9 mL, p=0.09). The PPV for fluid levels was 52% (95% CI 46-57) for significant HE. Increased density heterogeneity (p<0.01) and margin irregularity (p<0.01) were both associated with increased median absolute ICH volume at 24h. Increased density heterogeneity was associated with significant HE (χ2 =20.0, df=4, p<0.01); increased margin irregularity had a trend toward association with significant HE (χ2 =8.69, df=4, p=0.06). Conclusions: Density heterogeneity and fluid levels are associated with significant HE at 24h, and margin irregularity is associated with increased median hematoma size. These markers may be useful for predicting significant HE in ICH in cases where CTA is unavailable to identify the presence of a spot sign. They may also provide additional variables to incorporate into risk scores for hematoma expansion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".