Prenatal serum screening for Down syndrome and neural tube defects in the United States: Changes in utilization patterns from 2012 to 2020
Bibliographic record
Abstract
OBJECTIVE: To compile current usage of serum-based prenatal screening for Down syndrome in the United States and compare it with results from a similar 2011/2012 survey. SETTING: The College of American Pathologists maternal screening proficiency testing survey includes a supplemental question on the first of three yearly distributions. METHODS: Information regarding tests offered and the monthly number of pregnancies tested for US-based laboratories were reviewed. Results were stratified by size of laboratory, tests offered, and pregnancies tested. Findings were compared to an earlier survey. RESULTS: Fifty-six laboratories reported they will have screened 1,131,336 pregnancies in 2020. Of these, 36% are screened by stand-alone first trimester testing, 48% by stand-alone second trimester testing, and 16% using tests that integrate results from both trimesters. Eighty percent of all serum screens were provided by the five laboratories that performed the most screens (at least 50,000). These five performed similar proportions of first or second trimester screens (42.2% and 41.8%, respectively). Compared to eight years earlier, there are now 54% fewer laboratories. Pregnancies screened using the first trimester, second trimester, and integrated protocols were lower by 27%, 69%, and 72%, respectively. The serum screening activity in the US showed a 62% decrease from 2012 levels. During 2012-2020, the number of cell-free DNA tests increased from negligible to 1,492,332. CONCLUSIONS: Maternal serum screening for common aneuploidies has changed significantly in eight years with fewer laboratories, a shift toward larger laboratories and a 2.5-fold reduction in pregnancies tested, likely due to the introduction of cell-free DNA 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".