Volume Tolerance and Prognostic Impact of Hematoma Expansion in Deep and Lobar Intracerebral Hemorrhage
Bibliographic record
Abstract
BACKGROUND: The prognostic impact of intracerebral hemorrhage (ICH) volume varies according to location, with smaller volume tolerance in deep ICH, and hematoma expansion (HE) contributes to final ICH volume. We tested the hypothesis that HE influences outcome only when the final ICH volume achieves a critical threshold that differs according to ICH location. METHODS: Retrospective analysis of patients with supratentorial ICH admitted at 10 centers in North America and China (development cohort) and Europe (replication cohort). HE was defined as growth >33% and/or >6 mL. Location-specific (lobar versus deep) volume cutoffs for the prediction of poor outcomes were derived using receiver operating characteristic curves and the Youden index. The prognostic impact of HE stratified by location and final volume was explored with logistic regression (poor outcome: 90-day modified Rankin Scale score of 4–6), accounting for age, Glasgow Coma Scale, baseline volume, intraventricular hemorrhage, and admission center. RESULTS: We identified 1774 patients with ICH in the development cohort and 1746 in the replication cohort. A total of 1058 (mean age, 68 years; 47.8% men) and 1423 (mean age, 71 years; 44.7% men) subjects met the inclusion criteria, respectively. The optimal final ICH volume cutoff for poor outcome differed by location: ≥36 mL for lobar and ≥17 mL for deep ICH. HE with final volume below the cutoff was not associated with higher odds of poor outcome compared with patients without HE (adjusted odds ratio, 1.85 [95% CI, 0.78–4.38]; P =0.163 in lobar ICH; adjusted odds ratio, 0.85 [95% CI, 0.38–1.89]; P =0.685 in deep ICH). The combination of HE and final volume over the critical threshold was, however, significantly associated with poor prognosis, and the magnitude of this effect was substantial (adjusted odds ratio, 8.55 [95% CI, 2.87–25.48]; P <0.001 in lobar ICH; adjusted odds ratio, 10.34 [95% CI, 2.86–37.44]; P <0.001 in deep ICH). These findings were confirmed in the replication cohort. CONCLUSIONS: HE significantly impacts severe outcomes only when the final ICH volume exceeds a critical target threshold, and this threshold is lower in deep versus lobar ICH. These findings might inform clinical practice and future trials.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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 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".