Abstract 86: Location-Specific Hematoma Volume Tolerance for Spontaneous Intracerebral Hemorrhage
Bibliographic record
Abstract
Background: Hematoma volume is an important predictor of outcome in spontaneous intracerebral hemorrhage (ICH). Location-specific hematoma volume thresholds are associated with poor outcome and can inform surgical trial inclusion criteria and clinical decision rules for hematoma evacuation. In a pooled trial dataset, we evaluated associations between ICH location, hematoma volume thresholds, and patient outcomes. Methods: We performed a secondary analysis of the ATACH-2 clinical trial. We evaluated the associations between intraparenchymal location-specific hematoma volume cutoffs (thalamic, basal ganglia, and lobar) and poor outcome (mRS 4-6; primary outcome) or mortality (secondary outcome) at 3 months. Using semi-automated volumetric assessments of 24-hour CT scans, we applied volume cutoffs at 5 mL increments starting at ≤5 mL up to >50 mL. We also applied Youden’s method for each hematoma location to determine the optimal location-specific volume thresholds that predict outcomes. We calculated odds ratios (OR) of poor outcome through multivariable logistic regression models for each location, adjusted for age, sex, prior stroke/transient ischemic attack, hemisphere location, and intraventricular hemorrhage extension. Results: Out of 949 patients included for analysis, 358, 485 and 106 were diagnosed with thalamic, basal ganglia ICH and lobar ICH, respectively. Location-specific hematoma volume cutoffs most predictive of a poor outcome (mRS 4-6) calculated with Youden’s index were 6.6 mL for thalamic ICH (OR 5.72, 95% CI 3.22-10.18; p<0.0001), 23.1 mL for basal ganglia ICH (OR 14.11, 95% CI 8.21-24.27; p<0.0001) and 24.4 mL for lobar ICH (OR 8.22, 95% CI 2.3-29.36; p=0.0012). For our secondary outcome, Youden’s lobar ICH cutoff of 42.5mL was the most predictive of mortality. Predictive performances for Youden’s method are shown in Table 1, and for all thresholds shown in Figure 1. Conclusion: Hematoma volumes associated with poor outcome and mortality vary by location, supporting the notion that different brain regions have different “hematoma volume tolerances”; good outcomes are unlikely when ICH volume exceeds that brain region’s tolerance. Our results provide important data for location-specific hematoma volume tolerance to inform clinical trials and clinical decision rules in ICH management.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".