The recurrence risk of severe de novo pre‐eclampsia in singleton pregnancies: a population‐based cohort
Bibliographic record
Abstract
OBJECTIVE: Previous studies have found recurrence risks of severe pre-eclampsia as high as 40%. Our objective was to determine both the recurrence risk of severe de novo pre-eclampsia and risk factors associated with it in a contemporaneous population. STUDY DESIGN: Population-based retrospective cohort study. POPULATION: Women who had two or more singleton liveborn or stillborn hospital deliveries in Ontario, Canada between April 1994 and March 2002 and without a history of chronic hypertension. METHODS: International Classification of Disease codes were used to identify patients in the Canadian Institute for Health Information Discharge Abstract Database. MAIN OUTCOME MEASURES: The absolute and adjusted risks of recurrent severe de novo pre-eclampsia were determined. RESULTS: Between 1 April 1994 and 30 March 2002, there were 185 098 women with two or more singleton deliveries >20 weeks in the province of Ontario, Canada. There were 1954 women who had severe de novo pre-eclampsia in the index pregnancy, 133 of whom had recurrent severe pre-eclampsia, for a risk of recurrent severe pre-eclampsia of 6.8% (95% CI 5.7-7.9%). The risk of recurrent severe de novo pre-eclampsia was increased in women with pre-existing renal disease (adjusted OR 17.98, 95% CI 3.50-92.52) and those >35 years of age (adjusted OR 3.79, 95% CI 2.04-7.04, reference 20-25 years). CONCLUSIONS: The recurrence risk of severe de novo pre-eclampsia in our population-based cohort study (6.8%) is lower than previously published reports in selected populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".