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 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 | 0.000 | 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 | 0.001 |
| 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".