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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".