Supplementary Material for: Preeclampsia and onset of renal disorders in the long-term period following pregnancy
Bibliographic record
Abstract
Introduction: Preeclampsia is associated with acute renal complications during pregnancy, but the risk of renal sequelae later in life is unclear. We determined if preeclampsia was associated with chronic renal complications in the long-term period following pregnancy. Methods: We conducted a longitudinal cohort study of 1,431,156 pregnant women in Quebec, Canada with 25,598,024 person-years of follow-up between 1989 and 2023. The main exposure measure was preeclampsia, and outcomes included hospitalization for vascular and nonvascular renal complications up to 34 years after pregnancy. We estimated hazard ratios (HR) and 95% confidence intervals (CI) for the association between preeclampsia and subsequent kidney complications using Cox regression models adjusted for patient characteristics. Results: Patients with preeclampsia had a higher hospitalization rate for renal complications than patients without preeclampsia (29.4 vs. 19.5 per 10,000 person-years). Preeclampsia was associated with 1.45 times the risk of hospitalization for renal disease during follow-up (95% CI 1.40-1.50). Risks were particularly elevated for renal vascular disease (HR 3.74, 95% CI 3.21-4.37), diabetic kidney disease (HR 3.71, 95% CI 3.18-4.32), and glomerulopathy (HR 3.44, 95% CI 2.92-4.05). Associations were also present with obstructive uropathy (HR 1.44, 95% CI 1.30-1.58). Severe forms of preeclampsia, including early onset preeclampsia (HR 1.90, 95% CI 1.72-2.10) and superimposed preeclampsia (HR 2.52, 95% CI 2.22-2.85) were strongly associated with subsequent renal morbidity. Conclusion: Preeclampsia, especially severe preeclampsia, is associated with the long-term risk of renal disease. Patients with preeclampsia may benefit from nephrological follow-up in the long-term period 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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.787 | 0.179 |
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".