Dynamic Characterization of the CT Angiographic ‘Spot Sign’
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Standard (static) CT angiography is used to identify the intracerebral hemorrhage (ICH) spot sign. We used dynamic CT-angiography to describe spot sign characteristics and measurement parameters over 60-seconds of image acquisition. METHODS: We prospectively identified consecutive patients presenting with acute ICH within 4.5 hours of symptom onset, and collected whole brain dynamic CT-angiography (dCTA). Spot parameters (earliest appearance, duration, maximum Hounsfield unit (HU), time to maximum HU, time to spot diagnostic definition, spot volume and hematoma volumes) were measured using volumetric analysis software. RESULT: We enrolled 34 patients: three were excluded due to secondary causes of ICH. Of the remaining 31 patients there were 18 females (58%) with median age 70 (range 47-86) and baseline hematoma volume 33 ml (range 0.7-103 ml). Positive dCTA spot sign was present in 13 patients (42%) visualized as an expanding 3-dimensional structure temporally evolving its morphology over the scan period. Median time to spot appearance was 21 s (range 15-35 seconds). This method allowed tracking of spots evolution until the end of venous phase (active extravasation) with median duration of 39 s (range 25-45 seconds). The average density and time to maximum density was 204HU and 30.8 s (range 23-31 s) respectively. Median time to spot diagnosis was 20.8 s using either 100 or 120HU definitions. CONCLUSION: Dynamic CTA allows a 3-dimensional assessment of spot sign formation during acute ICH, and captured higher spot sign prevalence than previously reported. This is the first study to describe and quantify spot sign characteristics using dCTA; these can be used in ongoing and upcoming ICH studies.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".