Repeated measurement of pregnancy‐associated plasma protein‐A (PAPP‐A) in Down syndrome screening: A validation study
Bibliographic record
Abstract
OBJECTIVES: To confirm that measuring pregnancy-associated plasma protein-A (PAPP-A) in both first- and second-trimester serum samples improves Down syndrome screening. METHODS: We selected paired first- and second-trimester stored serum samples from 34 Down syndrome pregnancies (cases) and 514 unaffected pregnancies (controls) and tested the second-trimester samples for PAPP-A and dimeric inhibin-A (DIA). First-trimester PAPP-A measurements were already available, as were second-trimester measurements of alpha-fetoprotein, unconjugated estriol (uE3), and human chorionic gonadotrophin (hCG). RESULTS: PAPP-A was lower among cases than controls (0.47 MoM) in the first trimester (at an average of 12.5 weeks); in the second trimester, it was not different (0.91 MoM). Using repeated measures of PAPP-A alone, 21 of 34 cases were detected (62%, 95%CI 44% to 78%) with 5% false positives. At an observed 2% false-positive rate, the detection rates (DR) for the quadruple (69%) and serum integrated (69%) tests were lower than for the repeated measures test (75%). Modelled performance at 12 weeks was similar to these observed findings (70, 75, and 82%, respectively). If the first-trimester samples were collected at 10 weeks, however, DR would be higher (70, 81, and 91%, respectively). CONCLUSIONS: Adding a repeated measure of PAPP-A to existing serum markers improves Down syndrome screening to levels that are currently obtainable only by including ultrasound measurement of nuchal translucency (NT). Serum-based screening has the advantages of higher availability and reliability at a lower cost, resulting in a more effective screening strategy. A serum-based repeated measures test has a place in routine Down syndrome screening.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".