Comparison of risk factors and outcomes of gestational hypertension and pre-eclampsia
Bibliographic record
Abstract
BACKGROUND: It remains an enigma whether gestational hypertension (GH) and pre-eclampsia (PE) are distinct entities or different spectrum of the same disease. We aimed to compare the risk factors and outcomes between GH and PE. METHOD: A total of 7,633 pregnant women recruited between 12 and 20 weeks of gestation in the Ottawa and Kingston Birth Cohort from 2002 to 2009 were included in the analysis. Cox proportional hazards model was used to identify and compare the risk factors for GH and PE by treating gestational age at delivery as the survival time. Logistic regression model was used to compare outcome. Subgroup analysis was performed for early- and late-onset PE. RESULTS: GH and PE shared most risk factors including overweight and obesity, nulliparity, PE history, type 1 and 2 diabetes, and twin birth. Effect size of PE history (RR = 14.1 for GH vs. RR = 6.4 for PE) and twin birth (RR = 4.8 for GH vs. RR = 10.3 for PE) showed substantial difference. Risk factors modified gestational age at delivery in patients with GH and PE in similar pattern. Subgroup analysis showed that early- and late-onset PE shared some risk factors with different effect sizes, whereas folic acid supplementation showed protective effect for early-onset PE only. PE was strongly associated with several adverse outcomes including cesarean section, placental abruption, small for gestational age, preterm birth, and 5 min Apgar score < 7, whereas GH was associated with increased risk of preterm birth only. CONCLUSIONS: GH and PE shared common risk factors. Differences in effect sizes of risk factors and outcomes indicate that the conditions may have different pathophysiology and mechanism.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".