Lifetime Risk of First Symptomatic ICH or Seizure in Familial Cerebral Cavernous Malformations
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Familial cavernous malformations (FCMs) are vascular lesions that pose a lifelong risk of symptomatic hemorrhage (SH) and seizures, yet their natural history remains unclear. This study aims to determine the cumulative lifetime risk of a first SH and/or seizure and assess whether genetic variations influence these risks. METHODS: This international, multicenter retrospective cohort study included data from 16 tertiary referral centers and 1 patient advocacy group. Eligible patients had confirmed or suspected FCM, available magnetic resonance imaging (MRI) data, documented baseline clinical features, and longitudinal follow-up (FU). Functional outcomes were assessed using the modified Rankin Scale (mRS) at last FU. Direct adjusted survival curves and mixed-effects Cox regression analyses were performed to estimate cumulative lifetime risk. The association between genetic variations and SH/seizure rates was evaluated, and mixed-effects logistic regression assessed the effect of SH/seizures on mRS outcomes. RESULTS: (hazard ratio 1.799, 95% CI 1.008-3.208). SH and seizures were independently associated with worse mRS outcomes at last FU. DISCUSSION: variations exhibited a more aggressive disease course. Limitations include the non-population-based design, selection bias from tertiary centers, retrospective data collection, and variability in data extraction across centers. However, this study represents the largest international FCM cohort to date, improving the precision of risk estimates and providing valuable insights into disease progression.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".