Epilepsy risk among survivors of intensive care unit hospitalization for sepsis
Bibliographic record
Abstract
OBJECTIVE: To determine whether survivors of intensive care unit (ICU) hospitalizations with sepsis experience higher epilepsy risk than survivors of ICU hospitalizations without sepsis, and to identify sepsis survivors at highest risk. METHODS: We used linked, administrative health care databases to conduct a population-based, retrospective matched cohort study of adult Ontario residents discharged from an ICU between January 1, 2010, and December 31, 2015, identified using the Discharge Abstract Database. We used propensity scores to match patients who experienced sepsis during their index ICU hospitalization with up to 4 patients who did not experience sepsis. We applied marginal Cox proportional hazards regression to estimate the risk of epilepsy within 2 years following the index ICU hospitalization. Among sepsis survivors, Cox proportional hazards regression was used to identify factors associated with epilepsy. RESULTS: A total of 143,892 patients were included, 32,252 (22.4%) of whom were exposed. Sepsis survivors were at significantly higher epilepsy risk (hazard ratio [HR] 1.44, 95% confidence interval [CI] 1.15-1.80). The risk of epilepsy marginally decreased with increasing age (HR 0.97, 95% CI 0.96-0.99); patients with chronic kidney disease (HR 2.25, 95% CI 1.48-3.43) were at highest risk. CONCLUSIONS: In this real-world analysis, sepsis survivors, particularly those who are younger and have chronic kidney disease, are at significantly higher epilepsy risk. These findings indicate that sepsis may be an unrecognized epilepsy risk factor.
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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 | 0.000 | 0.001 |
| 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".