Abstract 77: Ongoing Leakage Revealed by Second Phase Spot Sign Volume Expansion in Multi-Phase Computed Tomography Angiography Strongly Predicts Intracranial Hemorrhage Growth
Bibliographic record
Abstract
Background: CTA spot sign is a predictor of intracerebral hemorrhage (ICH) expansion. This sign can fluctuate in appearance, volume, and timing. Multiphase CTA (mCTA) can identify spot sign through 3 time-resolved images. We sought to identify a novel predictor of follow up total hematoma expansion using mCTA. Methods: This cohort study included patients with ICH between 2012-2019. Quantomo software was used to measure total hematoma volume (ml) from baseline CT & follow-up CT/MRI blinded to spot sign in 3 mCTA phases. Spot sign expansion was calculated by subtracting 1 st phase spot sign volume from 2 nd phase spot sign volume measured in microliters. Results: 199 patients [63% male, mean age 69 years, median NIHSS 11, IQR 6-20] were included. Median baseline ICH volume was 16.1 ml (IQR 5-29.9 ml). Amongst all three mCTA phases, spot sign was best detected on the 2nd phase (23% vs 17.5% 1 st phase vs 22% 3 rd phase). In multivariable regression, spot sign expansion was significantly associated with follow up total hematoma expansion (OR: 1.03 per microliter of spot sign expansion, p=0.01). Figure 1 shows the predicted total hematoma expansion by spot sign expansion. mCTA spot sign had a higher sensitivity for predicting total hematoma volume expansion than single-phase CTA (reported in meta-analysis of 14 studies), 86% vs 53%, respectively, while both having similar specificity, 87% vs 88%, respectively. Conclusion: Spot sign expansion on mCTA is a novel predictor of total hematoma expansion and could be used to select patients for immediate therapeutic intervention in future clinical trials. Using mCTA improves sensitivity while preserving specificity over single-phase CTA.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".