Spot Sign Number Is the Most Important Spot Sign Characteristic for Predicting Hematoma Expansion Using First-Pass Computed Tomography Angiography
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The spot sign score (SSS) provides risk stratification for hematoma expansion in acute intracerebral hemorrhage; however, external validation is needed. We sought to validate the SSS and assess prognostic performance of individual spot characteristics associated with hematoma expansion from a prospective multicenter intracerebral hemorrhage study. METHODS: Two hundred twenty-eight intracerebral hemorrhage patients within 6 hours after ictus were enrolled in the Predicting Hematoma Growth and Outcome in Intracerebral Hemorrhage Using Contrast Bolus CT (PREDICT) study, a multicenter prospective intracerebral hemorrhage cohort study. Patients were evaluated with baseline noncontrast computerized tomography, computerized tomography angiography, and 24-hour follow-up computerized tomography. Primary outcome was significant hematoma expansion (>6 mL or >33%) and secondary outcome was absolute and relative expansion. Blinded computerized tomography angiography spot sign characterization and SSS calculation were independently performed by 2 neuroradiologists and a radiology resident. Diagnostic performance of the SSS and individual spot characteristics were examined with multivariable regression, receiver operating characteristic analysis, and tests for trend. RESULTS: SSS and spot number independently predicted significant, absolute, and relative hematoma expansion (P<0.05 each) and demonstrated near perfect interobserver agreement (κ=0.82 and κ=0.85, respectively). Incremental risk of hematoma expansion among spot-positive patients was not identified for SSS (P trend=0.720) but was demonstrated for spot number (P trend=0.050). Spot number and SSS demonstrated similar area under the curve (0.69 versus 0.68; P=0.306) for hematoma expansion. CONCLUSIONS: Multicenter external validation of the SSS demonstrates that the spot number alone provides similar prediction but improved risk stratification of hematoma expansion compared with the SSS.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".