Second and first trimester estimation of risk for Down syndrome: implementation and performance in the SAFER study
Bibliographic record
Abstract
OBJECTIVES: Document patient choices and screening performance (false positive and detection rates) when three improved Down syndrome screening protocols were introduced coincidentally. METHOD: Second-trimester 'triple marker' screening was expanded by adding second-trimester dimeric inhibin-A (four-marker), with or without first-trimester pregnancy-associated plasma protein-A (five-marker). Nuchal translucency (NT) measurements were included when available from accredited sonographers (six-marker). For assigning risk, two sets of marker distribution parameters were evaluated. RESULTS: Over 3.5 years, 8571 women enrolled (median age 30.6 years). Uptake of the four-, five- and six-marker protocols was 18%, 46% and 36%, respectively. Of those selecting an integrated test (five or six markers), 9.7% did not provide the second trimester serum sample. False positive rates decreased with added markers (5.2%, 5.1% and 2.5%, respectively) and varied between the two parameter sets, while detection remained high. Overall, 21 of 23 cases were detected (91%, 95% CI 73-98%) at a 4.2% false positive rate (95% CI 3.3-5.1%). CONCLUSIONS: Integrated screening protocols were chosen 4.6 times more often than four-marker screening (82% vs. 18% uptake). Overall detection was higher and false positives lower, consistent with recent guidelines. Important performance factors include gestational dating method, risk cut-off, and the parameter set used to assign risk.
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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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".