Changes in rate of preterm birth and adverse pregnancy outcomes attributed to preeclampsia after introduction of a refined definition of preeclampsia: A population‐based study
Bibliographic record
Abstract
INTRODUCTION: Since 2013, various guidelines for hypertension in pregnancy have been refined, no longer requiring proteinuria as a requisite criterion for preeclampsia. We aimed to evaluate the impact of the new definition on preterm birth (PTB) and adverse pregnancy outcomes. MATERIAL AND METHODS: Women delivering in Ontario between April 2012 and November 2016 were included. Delivery <24+0/7 weeks, major fetal anomalies or preexisting renal disease were excluded. The primary outcome was livebirth <37, <34 or <32 weeks. Rates, adjusted rate ratios (aRR) and ratio of the rate ratio (RRR) were used to compare outcomes in the 2 years after the new Society of Obstetricians and Gynaecologists of Canada (SOGC) guideline (December 2014-November 2016; period 2) vs the 2 years before (April 2012-March 2014; period 1), among women with and without preeclampsia. RESULTS: In all, 268 543 and 267 964 births in periods 1 & 2, respectively, were included. Respective preeclampsia rates increased significantly from 3.9% to 4.4% (p < 0.001), with no change in maternal morbidity rates. In preeclamptic women, respective rates of PTB <37 weeks were 21.0% and 20.7% (aRR 1.01, 95% confidence interval [CI] 1.00-1.02), with significant aRR for PTB <34 (0.86, 95% CI 0.77-0.96) and <32 weeks (0.79, 95% CI 0.67-0.94). A similar aRR was observed in women without preeclampsia. In preeclamptic women, composite severe neonatal morbidity decreased after guideline change (aRR 0.95, 95% CI 0.91-0.99), a finding not observed in women without preeclampsia (RRR 0.95, 95% CI 0.91-0.99). CONCLUSIONS: The new definition of preeclampsia was associated with increased disease rates, a modest reduction in adverse neonatal outcomes and no change in maternal outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".