Fetal Fraction Methodologies and Their Clinical Use: Results of a College of American Pathologists Exercise
Bibliographic record
Abstract
BACKGROUND: Noninvasive prenatal screening for common autosomal trisomies, sex chromosome aneuploidies and microdeletions vary by methodology and laboratory practice. The fetal portion of all cell-free DNA in the maternal circulation defines the fetal fraction (FF). The minimum specimen FF levels for reporting results vary between laboratories as well as the screening target (e.g., common trisomies vs select microdeletions). This variability can lead to confusion for both healthcare providers and patients. METHODS: Participants in the College of American Pathologists Non-Invasive Prenatal Testing 2021-B Exercise provided FF estimates for 3 manufactured samples. Responses to supplemental questions were also collected and analyzed. RESULTS: Overall, 72 of 77 participants responded. FF was measured by 66 participants using sequence counts (40), single nucleotide polymorphisms (15), fragment length (24), and Y chromosome sequences (24). Nearly half (48%) used multiple methods. For common trisomies, minimum FFs were none or <1% (n = 7), 1.0% to 3.9% (n = 35), 4.0% to 6.9% (n = 23), and ≥7.0% (n = 1); 4 participants did not measure FF. Challenge-specific FFs were variable with CVs of 13%, 15%, and 36%; the latter rate appears due to that sample's fetal karyotype of 47,XYY. Comparing adjusted FF results for the 3 samples shows that 85% of participant results were within 20% of the consensus. CONCLUSIONS: Using multiple methods to estimate FF was common, and cutoff levels for sample suitability varied widely. Within-laboratory FFs were less variable than between laboratories. Current FF estimates from clinical laboratories are not standardized and should be considered laboratory-specific.
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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 | 0.005 | 0.006 |
| 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 | 0.001 |
| 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".