Numbers of prenatal cell‐free DNA screens performed: Results of a 2022 CAP exercise
Bibliographic record
Abstract
OBJECTIVE: Determine current analytical methods and number of cell-free (cf) DNA prenatal screening tests performed for common trisomies. METHODS: The College of American Pathologists 2022-B Noninvasive Prenatal Testing exercise was distributed in December 2022 to 93 participants in 22 countries. Supplemental questions included the number of tests performed in a recent month and the proportion of samples originating outside the United States (US). RESULTS: Eighty-three participants from three continents returned results; 74 (89%) were suitable for the analyses. Nine manufacturer/platform combinations were identified, most commonly Illumina/Nextseq (55%). The most common methodology was whole genome sequencing (76%). Annualized cfDNA tests were 2.80 million, with Asian, European and North American participants representing 10.6%, 6.5% and 82.9% of tests, respectively. When restricted to US in-country tests, the annualized rate was 2.18 million, with four of 20 participants testing 79.2%. Among 73 respondents, 63 (86%) were for-profit, eight (11%) were non-profit academic or government supported and the remaining two included hospital-based and private non-profit. Eighteen (25%) supported relevant academic training. CONCLUSION: In 2011, screening for common trisomies was based on serum/ultrasound markers with an estimated 2.96 million US pregnancies screened in 131 laboratories. In 2022, cfDNA-based screening was offered by 20 laboratories testing 2.18 million US pregnancies.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.007 | 0.004 |
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".