P.146 Non-contrast CT markers of intracerebral hemorrhage expansion: a predictive accuracy and reliability study
Bibliographic record
Abstract
Background: We evaluated (1) the predictive accuracy and (2) multi-observer reliability of non-contrast CT markers of hematoma expansion (HE). Methods: In 124 patients with spontaneous intracerebral hemorrhage, two investigators documented the presence of six density (Barras density, hypodensity, black hole, swirl, blend, fluid level) and three shape (Barras shape, island, satellite) expansion markers, with discrepancies resolved by a third rater. We defined HE as any one of (1) >6 mL absolute or >33% relative growth of the intraparenchymal hematoma or (2) an absolute growth of >1 mL or new development of intraventricular hematoma. A subsample of 60 patients was used for the inter-observer reliability study in 13 raters. Seven raters participated in the intra-rater study. Results: The sensitivity of markers for HE varied between 4% (fluid level) and 78% (satellite), while specificity ranged from 37% (swirl) to 97% (black hole). Almost perfect inter-rater agreement was observed for the swirl (0.89) and fluid level (0.83) markers, while hypodensity (0.65) showed substantial agreement. Only the blend and fluid level markers achieved substantial intra-rater agreement (> 0.6) in all raters. Conclusions: Non-contrast CT markers of HE showed lower reliability and predictive accuracy than previously reported. Future studies should address means to improve NCCT-based HE prediction.
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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 | 0.007 | 0.032 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".