Abstract W P247: Dynamic Characterization of 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 collected consecutive patients with acute ICH presenting within 6 hours of symptom onset who underwent whole brain dynamic CT-angiography (dCTA). Spot parameters (earliest appearance, duration, Maximum Hounsfield unit (HU), time to maximum HU, time to spot diagnosis (based on HU>100 or >120) and 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. In the 31 patients with primary ICH, there were 13 males with median age 70 and baseline hematoma volume 33 ml. In our series, 13 patients (42 %) had positive dCTA spot sign. The spot was visualized as an expanding 3-dimensional structure that temporally evolved in its morphology over the scanning period. Median time to spot appearance was 21s (range 15-35 seconds). This method allowed tracking spots where they continued until end of venous phase (persistent leak) with median duration of 39s (range 25-45 seconds). The median maximum HU was 204, and median time to maximum HU was 30.8s. Median time to spot diagnosis was 20.8s using either the HU 100 or 120 definitions. Conclusion: Dynamic CT Angiography allows a 3-dimensional assessment of spot sign formation during acute ICH; this is the first study to quantitatively describe spot sign formation and morphology using dCTA.
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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) | 0.003 | 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".