Prediction of adverse pregnancy outcomes by combinations of first and second trimester biochemistry markers used in the routine prenatal screening of Down syndrome
Bibliographic record
Abstract
OBJECTIVE: To investigate the associations between four defined adverse pregnancy outcomes and levels of first and second trimester maternal serum markers focusing in particular on how well combinations of markers predict these adverse outcomes. METHODS: This was a retrospective review of associations between first and second trimester serum markers and adverse pregnancy outcomes among 141 698 women who underwent prenatal screening for Down syndrome in Ontario, Canada. Detection rates (DR), false positive rates (FPR), and odds ratios were estimated using both single and combinations of markers for the adverse outcomes defined. RESULTS: Women with decreased second trimester unconjugated oestriol (uE3), deceased first trimester maternal serum pregnancy-associated plasma protein A (PAPP-A), increased second trimester serum alpha fetoprotein (AFP), or increased second trimester total human chorionic gonadotrophin (hCG) were at greater risk of developing adverse pregnancy outcomes. At a 5% FPR, combinations of these markers predicted at best 33.3% of fetal loss and 31.5% of preterm births (PTB) before 32 weeks of gestation. CONCLUSION: There are significant associations between the levels of first and second trimester serum markers and adverse obstetric outcomes. However, even combinations of these markers can only predict adverse obstetric outcomes with modest accuracy.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".