Screening for pre‐eclampsia early in pregnancy: performance of a multivariable model combining clinical characteristics and biochemical markers
Bibliographic record
Abstract
OBJECTIVE: To investigate the performance of a multivariable model combining a priori clinical characteristics and biomarkers to detect, early in pregnancy, women at higher risk of developing pre-eclampsia (PE). DESIGN: Nested case-control study. SETTING: University medical centre, Quebec, Canada (CHU de Québec). POPULATION: A total of 7929 pregnant women recruited between 10 and 18 weeks of gestation. In all, 350 developed hypertensive disorders of pregnancy (HDP)-of which 139 had PE, comprising 68 with severe PE and 47 with preterm PE-and were matched with two women with a normal pregnancy. METHODS: We selected a priori clinical characteristics and promising markers to create multivariable logistic regression models: body mass index (BMI), mean arterial pressure (MAP), placental growth factor, soluble Fms-like tyrosine kinase-1, pregnancy-associated plasma protein A and inhibin A. MAIN OUTCOME MEASURES: PE, severe PE, preterm PE, HDP. RESULTS: At false-positive rates of 5 and 10%, the estimated detection rates were between 15% (5-29%) and 32% (25-39%), and between 39% (19-59%) and 50% (34-66%), respectively. Considering the low prevalence of PE in this population, the positive predictive values were 7% (5-9%) to 10% (7-13%) for PE and 2% (1-4%) to 4% (3-6%) in the preterm and severe PE subgroups. The multivariable model yielded areas under the receiver operating characteristics curves (AUC) between 0.72 (0.61-0.81) and 0.78 (0.68-0.88). When only BMI and MAP were included in the model, the AUC were similar to those of the a priori model. CONCLUSIONS: In a population with a low prevalence of preterm PE, a multivariable risk algorithm using an a priori combination of clinical characteristics and biochemical markers did not reach a performance justifying clinical implementation as screening test early in pregnancy.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".