First‐trimester Down syndrome screening using additional serum markers with and without nuchal translucency and cell‐free DNA
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate serum-only four-marker first trimester (1T-Quad) Down syndrome screening, alone or contingently to select 10-20% with highest risk for nuchal translucency (NT) or cell-free (cf)DNA. METHODS: Stored maternal serum samples (-80 °C) from 90 pregnancies with fetal Down syndrome and 1607 controls were retrieved and measured for placental growth factor, α-fetoprotein, pregnancy-associated plasma protein and free β-human chorionic gonadotropin. Samples were from singleton pregnancies (9-13 + 6 weeks), and NT was measured between 11 and 13 + 6 weeks. Markers were expressed in multiples of the normal median (MoM) for gestation. Gaussian models were fitted to the distribution of log MoMs by using observed parameters, standardized maternal age distribution (mean 27, SD 5.5) and published cfDNA results. RESULTS: The model-predicted detection rate (DR) for 1T-Quad was 74% [5% false-positive rate (FPR)]. When used contingently to select for NT, the DR was 89% at 5%. When used to select for cfDNA, the DR was 91% (FPR < 0.05%). CONCLUSION: The 1T-Quad test can achieve a similar DR to a second-trimester Quad test. When used contingently to select for NT, the DR is similar to the Combined test. Used contingently to select for cfDNA would achieve even higher detection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".