First-trimester screening for Down syndrome using quadruple maternal biochemical markers
Bibliographic record
Abstract
OBJECTIVES: Placental growth factor (PlGF) is used for first-trimester preeclampsia screening and could be combined with other biochemical markers for Down syndrome screening. We aim to estimate the predictive value of the combination of pregnancy-associated plasma protein (PAPP-A), free β-human chorionic gonadotropin (free β-hCG), placental growth factor (PlGF) and α-fetoprotein (AFP) with and without nuchal translucency. METHODS: Singleton pregnancies recruited at 11-14 weeks and followed until delivery. The four maternal markers were measured using Kryptor (ThermoFisher-BRAHMS) and adjusted for gestational age and maternal characteristics. The risk of Down syndrome was calculated using the Fetal Medicine Foundation algorithm and multivariate linear regression analyses in all cases and in 2,200 controls. Receiver-operator characteristic (ROC) curves were used to calculate the detection and false-positive rates. RESULTS: Twenty-six (0.2%) cases of Down syndrome were diagnosed among 13,386 participants. The combination of the four biomarkers could have detected 88% (95% CI: 72-97%) of the cases at a false-positive rate of 13% (95% CI: 12-15%). The addition of nuchal translucency would have increased the detection rate to 96% (95% CI: 82-99%) at a false-positive rate of 4% (95% CI: 4-5%) using a 1:300 cut-off and to 100% (95% CI: 89-100%) at a false-positive rate of 6% (95% CI: 5-8%) using a 1:500 cut-off. CONCLUSIONS: First-trimester screening using biochemical markers allows the identification of approximately 88% of Down syndrome cases for a false-positive rate of 13%. The addition of nuchal translucency raises the detection rate above 95% with a false-positive rate below 5%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".