B.6 Location-specific hematoma volume tolerances for spontaneous intracerebral hemorrhage
Bibliographic record
Abstract
Background: Location-specific hematoma volume thresholds are associated with poor outcomes and can inform surgical trial inclusion criteria and clinical decision rules for hematoma evacuation. Methods: We performed a secondary analysis of the ATACH-2 and INTERACT2 clinical trials. We evaluated the associations between intraparenchymal location-specific hematoma volume cutoffs (basal ganglia, thalamus and lobar) and poor outcome (modified Rankin Scale 4-6). Using 24-hour CT scans, we calculated Youden’s index 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. Results: Out of 1691 patients, 919, 551 and 221 were diagnosed with basal ganglia, thalamus and lobar intracerebral hemorrhage (ICH), respectively. Location-specific hematoma volume cutoffs most predictive of a poor outcome (mRS 4-6) were 22.24 mL for basal ganglia ICH (OR 4.82, 95% CI 3.19-7.27), 8.13 mL for thalamus ICH (OR 2.73, 95% CI 1.62-4.59) and 21.99 mL for lobar ICH (OR 6.31, 95% CI 2.53-15.74). Conclusions: Hematoma volumes associated with poor outcomes vary by location, supporting the idea that location-specific “hematoma volume tolerances” exist. Our results provide important data on location-specific hematoma volume tolerances to inform clinical trials 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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".