Abstract WMP92: Location of Hemorrhage Predicts Hematoma Expansion and Poor Clinical Outcome
Bibliographic record
Abstract
Background: Baseline volume, spot sign, and coagulation status all predict early hematoma expansion (HE) in intracerebral hemorrhage (ICH). However, the role of ICH location on HE remains unclear. We hypothesized that lobar-located ICH would facilitate HE as it provides a larger potential volume for expansion as compared to deep locations. However, due to the close proximity of critical structures and increased risk of ventricular rupture, we also hypothesized that deep ICH would have a paradoxically increased risk of mortality and morbidity. Our objective was to assess the effect of lobar vs. non-lobar hemorrhage on HE and clinical outcome. Methods: We analyzed data from the prospective multicentre PREDICT study where patients with ICH presenting to hospital under 6 hours of symptom onset received a baseline CT, CTA, 24 hour follow-up CT, and 90-d mRS. ICH location was categorized as lobar vs deep, and primary outcomes were significant HE (>6mL) and poor clinical outcome (mRS >3). Multivariable regression with stepwise selection was used to adjust for relevant covariates. Sensitivity analysis was conducted by expanding the inclusion criteria to include patients who died or were treated with Factor VIIa and/or surgery prior to follow-up CT. Results: Among 302 patients meeting the inclusion criteria, lobar hemorrhage was associated with increased hematoma expansion >6mL (p=0.003), poor clinical outcome (p=0.011) and mortality (p=0.017). When adjusted for covariates, lobar hemorrhage independently predicted significant hematoma expansion (aOR 2.3 [95% CI: 1.2-4.4], p=0.02). Sensitivity analysis included a total of 353 patients and lobar location was no longer significantly associated with poor outcome (p=0.198). This appeared to be related to a higher proportion of IVH in the excluded population (33% Primary vs. 65% Excluded, p<0.001). Conclusion: Lobar hemorrhage led to expansion and poor clinical outcome in the primary analysis population. Sensitivity analysis of the excluded population revealed that deep bleeds are associated with a higher degree of mortality and morbidity, likely due to a higher frequency of IVH. Our findings suggest that baseline ICH location should be considered for risk stratification algorithms.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".