Prevalence and the predictive performance of the dynamic CT-angiography spot sign in an observational cohort with intracerebral hemorrhage
Bibliographic record
Abstract
The CT-angiography (CTA) spot sign is a predictor of hematoma expansion (HE). We have previously reported on the use of dynamic CTA (dCTA) to detect spot sign, and to study its formation over the acquisition period. In this study, we report the frequency of dCTA spot sign in acute intracerebral hemorrhage, its sensitivity and specificity to predict HE, and explore the rate of contrast extravasation in relation to hematoma growth.We enrolled consecutive patients presenting with primary intracerebral hemorrhage within 4.5 hours. All patients underwent a dCTA protocol acquired over 60 seconds following contrast injection. We calculated frequency of the dCTA spot sign, predictive performance, and rate of contrast extravasation. We compared extravasation rates to the dichotomous definition of significant HE (defined as 6 mL or 33% growth).In 78 eligible patients, dCTA spot sign frequency was 44.9%. In 61 patients available for expansion analysis, sensitivity and specificity of dCTA spot sign was 65.4% and 62.9%, respectively. Contrast extravasation rate did not significantly predict HE (Odds Ratio 15.6 for each mL/min [95% confidence interval 0.30-820.25], P = .17). Correlation between extravasation rate and HE was low (r = 0.297, P= .11). Patients with significant HE had a higher rate of extravasation as compared to those without (0.12 mL/min vs 0.04 mL/min, P = .03).Dynamic CTA results in a higher frequency of spot sign positivity, but with modest sensitivity and specificity to predict expansion. Extravasation rate is likely related to HE, but a single measurement may be insufficient to predict the magnitude of expansion.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".