Maternal Morbidity Associated With Early-Onset and Late-Onset Preeclampsia
Bibliographic record
Abstract
OBJECTIVE: To examine temporal trends in early-onset compared with late-onset preeclampsia and associated severe maternal morbidity. METHODS: The study included all singleton deliveries in Washington State between 2000 and 2008 (N=670,120). Preeclampsia onset was determined using hospital records linked to birth certificates. Severe maternal morbidity was defined as any potentially life-threatening condition. Logistic regression was used to obtain adjusted odds ratios (aOR) and 95% confidence intervals (95% CI). RESULTS: The preeclampsia rate was 3.0 per 100 singleton births, and increased slightly from 2.9 to 3.1 between 2000 and 2008. Rates of early-onset and late-onset disease were 0.3% and 2.7%, respectively. The temporal increase was significant only for early-onset disease (4.5%/year; 95% CI 2.3-5.8%) after adjustment for changes in maternal characteristics. Maternal death rates were higher among women with early-onset (42.1/100,000 deliveries) and late-onset preeclampsia (11.2/100,000) compared with women without preeclampsia (4.2/100,000). The rate of severe maternal morbidity (excluding obstetric trauma) was 12.2 per 100 deliveries in the early-onset group (aOR 3.7, 95% CI 3.2-4.3), 5.5 per 100 deliveries in the late-onset group (aOR 1.7, 95% CI 1.6-1.9), and approximately 3 per 100 in women without preeclampsia. Early-onset preeclampsia conferred a substantially higher risk of cardiovascular, respiratory, central nervous system, renal, hepatic, and other morbidity. However, rates of obstetric trauma were significantly lower among women with preeclampsia. CONCLUSION: Women with early-onset and late-onset preeclampsia have significantly higher rates of specific maternal morbidity compared with women without early-onset and late-onset disease. LEVEL OF EVIDENCE: : II.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".