First trimester screening for Down syndrome using nuchal translucency, maternal serum pregnancy‐associated plasma protein A, free‐β human chorionic gonadotrophin, placental growth factor, and α‐fetoprotein
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the screening performance for Down syndrome using first trimester combined screening (FTS) and two additional markers, serum placental growth factor (PlGF) and α-fetoprotein (AFP). METHODS: This is a retrospective case-control study of 137 pregnancies affected by Down syndrome and 684 individually matched unaffected pregnancies. Stored serum samples were tested for all four markers, and results were expressed as multiples of the gestation-specific median (MoM). Multivariate Gaussian modeling was used to calculate risks for different combinations of markers and to predict the detection rate (DR) and false positive rate (FPR). The predicted performance of enhanced FTS (FTS plus PlGF and AFP) was compared with FTS; the performance without nuchal translucency (first trimester quad) was assessed. RESULTS: For affected pregnancies, the median PlGF level was 0.622 MoM and median AFP 0.764 MoM. Adding PlGF and AFP improved the screening performance. At 3% FPR, DR increased by 4.4% from 83.8% to 88.2% using enhanced FTS; at 95% DR, FPR decreased by 8.3%, from 19.3% to 11.0%. At 3% FPR, DR using first trimester quad test was 76.4%. CONCLUSIONS: The performance of FTS can be enhanced by adding PlGF and AFP. Even without nuchal translucency, the test would perform well.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".