Non-contrast CT markers of intracerebral hemorrhage expansion: The influence of onset-to-CT time
Bibliographic record
Abstract
BACKGROUND: Hematoma expansion (HE) is an appealing therapeutic target in intracerebral hemorrhage (ICH) and non-contrast computed tomography (NCCT) features are promising predictors of HE. AIMS: We investigated whether onset-to-CT time influences the diagnostic performance of NCCT markers for HE. METHODS: Retrospective multicentre analysis of patients with primary ICH. The following NCCT markers were analyzed: hypodensities, heterogeneous density, blend sign, and irregular shape. HE was defined as growth ⩾6 mL and/or ⩾33%. We calculated the sensitivity, specificity, positive, and negative predictive values (PPVs and NPVs) of NCCT markers for HE, stratified by onset-to-CT time (<2 h, 2-4 h, 4-6 h, >6 h). RESULTS: We included 1135 patients (median age 69, 53% males), of whom 307 (27%) experienced HE.Overall hypodensities had the highest sensitivity (0.68) and blend sign the highest specificity (0.87) for HE. Hypodensities were more common and had higher sensitivity (0.80) in patients with imaging within 2 h. The same result was observed for heterogeneous density, whereas irregular shape had a similar prevalence across time strata and higher sensitivity (0.79) beyond 6 h from onset. The frequency of blend sign increased with longer onset-to-CT time, whereas its specificity declined after 6 h from onset. CONCLUSION: The diagnostic performance of NCCT markers is influenced by imaging time. Hypodensities identified four out of five patients with HE within 2 h from onset, whereas irregular shape performed better in late presenters. Our findings may improve the use of NCCT markers in future studies and trials targeting HE.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".