Risk of seizures and status epilepticus in older patients with liver disease
Bibliographic record
Abstract
OBJECTIVE: Seizures can be provoked by systemic diseases associated with metabolic derangements, but the association between liver disease and seizures remains unclear. METHODS: We performed a retrospective cohort study using inpatient and outpatient claims between 2008 and 2015 from a nationally representative 5% sample of Medicare beneficiaries. The primary exposure variable was cirrhosis, and the secondary exposure was mild, noncirrhotic liver disease. The primary outcome was seizure, and the secondary outcome was status epilepticus. Diagnoses were ascertained using validated International Classification of Diseases, Ninth Edition, Clinical Modification codes. Survival statistics were used to calculate incidence rates, and Cox proportional hazards models were used to examine the association between exposures and outcomes while adjusting for seizure risk factors. RESULTS: Among 1 782 402 beneficiaries, we identified 10 393 (0.6%) beneficiaries with cirrhosis and 19 557 (1.1%) with mild, noncirrhotic liver disease. Individuals with liver disease were older and had more seizure risk factors than those without liver disease. Over 4.6 ± 2.2 years of follow-up, 49 843 (2.8%) individuals were diagnosed with seizures and 25 patients (0.001%) were diagnosed with status epilepticus. Cirrhosis was not associated with seizures (hazard ratio [HR] = 1.1, 95% confidence interval [CI] = 1.0-1.3), but there was an association with status epilepticus (HR = 1.9, 95% CI = 1.3-2.8). Mild liver disease was not associated with a higher risk of seizures (HR = 0.8, 95% CI = 0.6-0.9) or status epilepticus (HR = 1.1, 95% CI = 0.7-1.5). SIGNIFICANCE: In a large, population-based cohort, we found an association between cirrhosis and status epilepticus, but no overall association between liver disease and seizures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".