Long-term risk of chronic liver disease after pre-eclampsia
Bibliographic record
Abstract
BACKGROUND: Pre-eclampsia is associated with acute hepatic complications, but the risk of developing chronic liver disease is unclear. We determined whether pre-eclampsia was associated with chronic liver disease for up to three decades after pregnancy. METHODS: We conducted a longitudinal population-based cohort study of 1 460 099 pregnant women with and without pre-eclampsia in Quebec, Canada, between 1989 and 2022. The main exposure was pre-eclampsia in any pregnancy. Outcomes included hospitalization for liver disease during 26 275 081 person-years of follow-up after pregnancy. Using Cox regression models adjusted for age, comorbidity, and socio-economic disadvantage, we estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between pre-eclampsia and liver disease. RESULTS: Women with pre-eclampsia had a higher incidence of hospitalization for liver disease than women without pre-eclampsia (119.3 vs. 61.5 per 100 000 person-years). During a median follow-up of 18.4 years, pre-eclampsia was associated with 1.85 times the risk of hospitalization for liver disease (95% CI 1.75-1.95). Early-onset pre-eclampsia (HR 2.36, 95% CI 2.04-2.74), superimposed pre-eclampsia (HR 2.78, 95% CI 2.30-3.36), and pre-eclampsia recurring in more than one pregnancy (HR 3.00, 95% CI 2.59-3.46) were strongly associated with liver hospitalization. Associations were present with several liver complications, including hepatic cirrhosis, chronic hepatitis, fatty liver disease, and hepatic failure. Pre-eclampsia was more strongly associated with hepatic hospitalization within 5 years of pregnancy, although risks persisted for up to 33 years later. CONCLUSION: Pre-eclampsia is associated with the long-term risk of liver disease up to 33 years after pregnancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".