Preeclampsia and the long term risk of developing neurological disorders requiring hospital admission
Bibliographic record
Abstract
INTRODUCTION: Preeclampsia is associated with acute neurological complications during pregnancy, but the subsequent risk of developing a neurological disorder is unclear. We determined if preeclampsia was associated with the long-term risk of neurological morbidity. METHODS: We conducted a longitudinal cohort study of 1,460,098 pregnant women with and without preeclampsia in QC, Canada, between 1989 and 2023. The main exposure measure was preeclampsia diagnosed in any pregnancy. Outcomes included hospitalization for cerebrovascular disease, epilepsy, and other neurological disorders up to 3 decades after pregnancy. Using Cox regression models adjusted for confounders, we estimated hazard ratios (HR) and 95% confidence intervals (CI) for the association between preeclampsia and neurological disorders during 27,659,555 person-years of follow-up. RESULTS: There were 1,460,098 women in the cohort, including 73,890 (5.1%) with preeclampsia. Women with preeclampsia had a higher incidence of neurological disorders than women without preeclampsia (113.2 vs. 79.3 per 100,000 person-years). Compared with no preeclampsia, preeclampsia was associated with 1.49 times the risk of later neurological hospitalization (95% CI 1.41-1.57). Preeclampsia was primarily associated with cerebrovascular disease (HR 1.89, 95% CI, 1.76-2.03) and epilepsy (HR 1.39, 95% CI, 1.24-1.57). A link with other neuropathology was less apparent, although severe preeclampsia was associated with neurodegenerative disorders. Severe hypertension, including early onset (HR 2.35, 95% CI, 2.06-2.68), recurrent (HR 2.47, 95% CI, 2.13-2.86), and superimposed preeclampsia (HR 2.60, 95% CI, 2.17-3.12), was more strongly associated with neurological hospitalization overall. CONCLUSION: Preeclampsia is associated with the long-term risk of developing cerebrovascular disease and epilepsy, but associations with other neurological disorders are less prominent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".