Small intracerebral hemorrhages have a low spot sign prevalence and are less likely to expand
Bibliographic record
Abstract
BACKGROUND: Hematoma expansion is a major predictor of morbidity and mortality after intracerebral hemorrhage (ICH). Both baseline hematoma volume and the CT-angiogram (CTA) spot sign predict hematoma expansion. Because the CTA spot sign may represent foci of active hemorrhage, we hypothesized that patients with smaller baseline hematoma volumes are less likely to be spot sign positive, and therefore less likely to expand. AIM: We sought to validate our prior finding that small hematomas are unlikely to expand, and to determine the relationship between baseline hematoma volume, spot sign status, and risk of hematoma expansion. METHODS: Data were from the prospective PREDICT ICH study. Patients presenting within 6 h of symptom onset with completed baseline CT, CTA, and follow-up CT were included. Baseline hematoma volume was categorized a priori (<3 mL, 3-10 mL, 10-20 mL, >20 mL). The primary outcome was significant hematoma expansion (≥6 mL, ≥12.5 mL or ≥33%) and secondary outcomes were early neurological worsening, good clinical outcome (modified Rankin Scale 0-3), and mortality at 90 days. RESULTS: Among 315 patients meeting the inclusion criteria, baseline hematoma volume category predicted absolute hematoma expansion (p < 0.001), spot sign prevalence (p < 0.001), early neurologic worsening (p = 0.002), clinical outcome (p < 0.001), and mortality (p < 0.001). Very small hematomas (<3 mL) were unlikely to be spot positive (7.7%), unlikely to expand (2.6%), and were associated with a 73% chance of good clinical outcome. Spot sign appeared to be most predictive of expansion in the 3-10 mL baseline hematoma volume category. CONCLUSION: Very small hematomas are unlikely to expand and have a low spot sign prevalence. Hemostatic therapy trials may be best targeted at hemorrhages >3 mL in volume.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".