Intracerebral hemorrhage: toward physiological imaging of hemorrhage risk in acute and chronic bleeding
Bibliographic record
Abstract
Despite improvements in management and prevention of intracerebral hemorrhage (ICH), there has been little improvement in mortality over the last 30 years. Hematoma expansion, primarily during the first few hours is highly predictive of neurological deterioration, poor functional outcome, and mortality. For each 10% increase in ICH size, there is a 5% increase in mortality and an additional 16% chance of poorer functional outcome. As such, both the identification and prevention of hematoma expansion are attractive therapeutic targets in ICH. Previous studies suggest that contrast extravasation seen on CT Angiography (CTA), MRI, and digital subtraction angiography correlates with hematoma growth, indicating ongoing bleeding. Contrast extravasation on the arterial phase of a CTA has been coined the CTA Spot Sign. These easily identifiable foci of contrast enhancement have been identified as independent predictors of hematoma growth, mortality, and clinical outcome in primary ICH. The Spot Sign score, developed to stratify risk of hematoma expansion, has shown high inter-observer agreement. Post-contrast leakage or delayed CTA Spot Sign, on post contrast CT following CTA or delayed CTA respectively are seen in an additional ∼8% of patients and explain apparently false negative observations on early CTA imaging in patients subsequently undergoing hematoma expansion. CT perfusion provides an opportunity to acquire dynamic imaging and has been shown to quantify rates of contrast extravasation. Intravenous recombinant factor VIIa (rFVIIa) within 4 h of ICH onset has been shown to significantly reduce hematoma growth. However, clinical efficacy has yet to be proven. There is compelling evidence that cerebral amyloid angiopathy (CAA) may precede the radiographic evidence of vascular disease and as such contribute to microbleeding. The interplay between microbleeding, CAA, CTA Spot Sign and genetic composition (ApoE genotype) may be crucial in developing a risk model for ICH.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".