Abstract 193: Cerebral Cavernous Malformation Location and Mode of Presentation Predict the Risk of Hemorrhage During Their Untreated Clinical Course: Individual Patient Data Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Cerebral cavernous malformations (CCM) are the second commonest incidental finding on brain MRI and may cause symptomatic intracranial hemorrhage (ICH). However, the risk and predictors of ICH from CCM remain uncertain. HYPOTHESIS: Presentation with ICH or non-hemorrhagic focal neurological deficit (FND) attributable to CCM, brainstem CCM location, female sex, increasing age and multiple CCM predict ICH occurrence during follow-up without CCM treatment. METHODS: Three hospital-based cohorts and two cohorts from a population-based study provided individual patient data on clinical course from CCM diagnosis until either first CCM treatment or last available follow-up. We used survival analysis of each cohort to estimate the 5-year risk of symptomatic ICH or new FND, multivariable Cox regression to identify baseline predictors of outcome, and random-effects models to pool estimates in meta-analysis. RESULTS: Among 988 adults who experienced 62 ICHs during 3,232 person-years of follow-up, clinical presentation with ICH or FND (hazard ratio [HR] 7.4, 95% confidence interval [CI] 2.9-19.2) and brainstem CCM location (HR 5.7, 95% CI 3.2-10.3) were associated with a higher risk of a first ICH within five years of CCM diagnosis, but age, sex and CCM multiplicity were not. The 5-year estimated risk of ICH during untreated follow-up was 2.4% (95% CI 1.0-3.8) for 566 adults without ICH/FND from CCM outside the brainstem, 5.5% (95% CI 0-12.9) for 51 adults without ICH/FND from brainstem CCM, 10.6% (95% CI 5.1-16.1) for 198 adults with ICH/FND from CCM outside the brainstem, and 25.7% (95% CI 18.3-33.1) for 173 adults with ICH/FND from brainstem CCM. In secondary analyses of first ICH or FND, event rates increased but predictors remained unchanged. CONCLUSION: Mode of clinical presentation and CCM location are independently associated with ICH or FND within five years of CCM diagnosis, which can inform decisions about CCM treatment.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.042 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".