Co-localization of NCCT hypodensity and CTA spot sign predicts substantial intracerebral hematoma expansion: The Black-&-White sign
Bibliographic record
Abstract
Abstract Background: Existing radiological markers of hematoma expansion (HE) show modest predictive accuracy. We aim to investigate a novel radiological marker that co-localizes findings from non-contrast CT (NCCT) and CT angiography (CTA) to predict HE. Methods: Consecutive acute intracerebral hemorrhage patients admitted at Foothills Medical Centre in Calgary, Canada, were included. The Black-&-White sign was defined as any visually identified spot sign on CTA co-localized with a hypodensity sign on the corresponding NCCT. The primary outcome was hematoma expansion (⩾6 mL or ⩾33%). Secondary outcomes included absolute (<3, 3–6, 6–12, ⩾12 mL) and relative (0%, <25%, 25%–50%, 50%–75%, or >75%) hematoma growth scales. Results: Two-hundred patients were included, with 50 (25%) experiencing HE. Forty-four (22%) showed the spot sign, 69 (34.5%) the hypodensity sign, and 14 (7%) co-localized both as the Black-&-White sign. Those with the Black-&-White sign had higher proportions of HE (100% vs 19.4%, p < 0.001), greater absolute hematoma growth (23.37 mL (IQR = 15.41–30.27) vs 0 mL (IQR = 0–2.39), p < 0.001) and relative hematoma growth (120% (IQR = 49–192) vs 0% (0–15%), p < 0.001). The Black-&-White sign had a specificity of 100% (95%CI = 97.6%–100%), a positive predictive value of 100% (95%CI = 76.8%–100%), and an overall accuracy of 82% (95%CI = 76%–87.1%). Among the 14 patients with the Black-&-White sign, 13 showed an absolute hematoma growth ⩾12 mL, and 10 experienced a HE exceeding 75% of the initial volume. The inter-rater agreement was excellent (kappa coefficient = 0.84). Conclusion: The Black-&-White sign is a robust predictor of hematoma expansion occurrence and severity, yet further validation is needed to confirm these compelling findings.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".