Sequential Pathways of Testing After First-Trimester Screening for Trisomy 21
Bibliographic record
Abstract
OBJECTIVE: To evaluate the performance and use of second-trimester multiple-marker maternal serum screening for trisomy 21 by women who had previously undergone first-trimester combined screening (nuchal translucency, pregnancy-associated plasma protein A, and free beta-hCG), with disclosure of risk estimates. METHODS: In a multicenter, first-trimester screening study sponsored by the National Institute of Child Health and Human Development, multiple-marker maternal serum screening with alpha-fetoprotein, unconjugated estriol, and total hCG was performed in 4,145 (7 with trisomy 21) of 7,392 (9 with trisomy 21) women who were first-trimester screen-negative and 180 (7 with trisomy 21) of 813 (52 with trisomy 21) who were first-trimester screen-positive. Second-trimester risks were calculated using multiples of the median and a standardized risk algorithm with a cutoff risk of 1:270. RESULTS: Among the first-trimester screen-negative cohort, 6 of 7 (86%) trisomy 21 cases were detected by second-trimester multiple-marker maternal serum screening with a false-positive rate of 8.9%. Among the first-trimester screen-positive cohort, all 7 trisomy 21 cases were also detected in the second trimester, albeit with a 38.7% false-positive rate. CONCLUSION: Our data demonstrate that a sequential screening program that provides patients with first-trimester results and offers the option for early invasive testing or additional serum screening in the second trimester can detect 98% of trisomy 21-affected pregnancies. However, such an approach will result in 17% of patients being considered at risk and, hence, potentially having an invasive test. LEVEL OF EVIDENCE: II-2
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| 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 | 0.001 | 0.001 |
| 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".