Spot Sign in Intracerebral Hemorrhage: Critical Reappraisal and Future Clinical Implications
Bibliographic record
Abstract
Hematoma expansion (HE) is a common occurrence affecting around 10% to 30% of patients with acute intracerebral hemorrhage within the initial hours from symptom onset and is the only modifiable factor associated with poor clinical outcomes. The detection of contrast extravasation on computed tomography (CT) angiography, known as the spot sign, was initially embraced as a promising radiological marker for predicting HE that could aid patient selection for acute interventions aimed at minimizing HE. However, the initial enthusiasm waned as clinical studies failed to show clear clinical benefits of hemostatic treatments when patients were selected based on the presence of this imaging marker. In this narrative review, we provide a comprehensive summary of the pathophysiology, definitions, imaging protocols, and predictive performance of the spot sign, along with the clinical studies that have selected and treated patients based on its presence. Finally, we delve into some nuances of the spot sign that can enhance its predictive performance and help stratify HE risk with greater precision. These features include static findings observed on single-phase CT angiography (ie, number, volume, CT density, and colocalization with hypodensities), as well as dynamic findings identified on multiphase/dynamic CT angiography (ie, timing of appearance, volume increase, volume decrease for tissue dispersion, and CT density changes). In this reappraisal of the spot sign, we aim to reinvigorate research on advanced neuroimaging in intracerebral hemorrhage that could lead to a more accurate HE prediction. This could facilitate better selection for therapies aimed at preventing HE or surgical approaches to address the bleeding source.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".