Preeclampsia and Long-term Risk of Maternal Retinal Disorders
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether preeclampsia is associated with risk of maternal retinal disease in the decades after pregnancy. METHODS: We carried out a longitudinal cohort study of 1,108,541 women who delivered neonates in any hospital in Quebec, Canada, between 1989 and 2013. We tracked women for later hospitalizations until March 31, 2014. Preeclampsia was measured at delivery categorized by severity (mild or severe) and onset (before or at 34 weeks or more of gestation). Main outcomes were hospitalizations and inpatient procedures for retinal detachment, retinopathy, or other retinal disorders. We used Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) comparing preeclampsia with no preeclampsia adjusting for diabetes and hypertension. RESULTS: Compared with no preeclampsia, women with preeclampsia had a higher incidence of hospitalization for retinal detachment (52.9 compared with 23.9/10,000), retinopathy (60.5 compared with 8.0/10,000), and other retinal disorders (13.3 compared with 7.3/10,000). Preeclampsia was most strongly associated with traction detachments (HR 2.39, 95% CI 1.52-3.74), retinal breaks (HR 2.48, 95% CI 1.40-4.41), and diabetic retinopathy (HR 4.13, 95% CI 3.39-5.04). Severe and early-onset preeclampsia was associated with even higher risk compared with mild or late-onset preeclampsia. CONCLUSION: Preeclampsia, particularly severe or early-onset preeclampsia, is associated with an increased risk of maternal retinal disease in the decades after pregnancy.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".