Abstract P411: Prevalence, Characteristics and Outcomes of Undetermined Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: We sought to determine the prevalence, radiological characteristics, and clinical outcomes of intracerebral hemorrhage (ICH) of undetermined etiology. Methods: Systematic review and meta-analysis of studies involving patients with spontaneous ICH was conducted to assess the prevalence and clinical-radiological characteristics of undetermined ICH. Additionally, we assessed the rates for ICH secondary to hypertensive arteriopathy (HTN-A) and cerebral amyloid angiopathy (CAA). Subgroup analyses were performed based on the use of a) etiology-oriented ICH classification, b) detailed neuroimaging, and c) Boston criteria among CAA-ICH. Results: 24 studies were included (n=15,828; mean age: 64.8 years, males: 60.8%). The pooled prevalence of HTN-A ICH, undetermined ICH and CAA-ICH were 50% (95%CI: 43-58%), 18% (95%CI: 13-23%), and 12% (95%CI: 7-17%; p<0.001 between subgroups). The volume of ICH was largest in CAA-ICH 24.7mL (95%CI: 19.7-29.8mL), followed by HTN-A ICH 16.2mL (95%CI: 10.9-21.5mL) and undetermined ICH 15.4mL (95%CI: 6.2-24.5mL). Among patients with undetermined ICH, the rates of short-term mortality and intraventricular hemorrhage were 33% (95%CI: 25-42%) and 38% (95%CI: 28-48%), respectively. Subgroup analysis demonstrated a higher rate of undetermined ICH among studies that did not use an etiology-oriented classification (22%; 95%CI: 15-29%). No difference was observed between studies based on the completion of detailed neuroimaging to assess the rates of undetermined ICH (p=0.62). Conclusions: The etiology of spontaneous ICH remains undetermined among one in five patients in studies using etiology-oriented classification and among one in four patients in studies that avoid using etiology-oriented classification. The short-term mortality in undetermined ICH is high despite the relatively small ICH volume. Our findings suggest the use of etiology-oriented classification to approach ICH patients (Figure).
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".