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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".