Do Intracerebral Hemorrhage Nonexpanders Actually Expand Into the Ventricular Space?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The computed tomographic angiography spot sign as a predictor of hematoma expansion is limited by its modest sensitivity and positive predictive value. It is possible that hematoma expansion in spot-positive patients is missed because of decompression of intracerebral hemorrhage (ICH) into the ventricular space. We hypothesized that revising hematoma expansion definitions to include intraventricular hemorrhage (IVH) expansion will improve the predictive performance of the spot sign. Our objectives were to determine the proportion of ICH nonexpanders who actually have IVH expansion, determine the proportion of false-positive spot signs that have IVH expansion, and compare the known predictive performance of the spot sign to a revised definition incorporating IVH expansion. METHODS: We analyzed patients from the multicenter PREDICT ICH spot sign study. We defined hematoma expansion as ≥6 mL or ≥33% ICH expansion or >2 mL IVH expansion and compared spot sign performance using this revised definition with the conventional 6 mL/33% definition using receiver operating curve analysis. RESULTS: Of 311 patients, 213 did not meet the 6-mL/33% expansion definition (nonexpanders). Only 13 of 213 (6.1%) nonexpanders had ≥2 mL IVH expansion. Of the false-positive spot signs, 4 of 40 (10%) had >2 mL ventricular expansion. The area under the curve for spot sign to predict significant ICH expansion was 0.65 (95% confidence interval, 0.58-0.72), which was no different than when IVH expansion was added to the definition (area under the curve, 0.66; 95% confidence interval, 0.58-0.71). CONCLUSIONS: Although IVH expansion does indeed occur in a minority of ICH nonexpanders, its inclusion into a revised hematoma expansion definition does not alter the predictive performance of the spot sign.
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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 | 0.003 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".