Effect of preexamination conditions in a centralized-testing model of non-invasive prenatal screening
Bibliographic record
Abstract
OBJECTIVES: Non-invasive prenatal testing requires the presence of fetal DNA in maternal plasma. Understanding how preexamination conditions affect the integrity of cell-free DNA (cfDNA) and fetal fraction (FF) are a prerequisite for test implementation. Therefore, we examined the adjusted effect that EDTA and Streck tubes have on the cfDNA quantity and FF. METHODS: A total of 3,568 maternal blood samples across Canada were collected in either EDTA, or Streck tubes, and processing metrics, maternal body mass index (BMI), gestational age and fetal karyotype and sex were recorded. Plasma samples were sequenced using two different sequencing platforms in separate laboratories. Sequencing data were processed with SeqFF to estimate FF. Linear regression and multivariate imputation by chained equations were used to estimate the adjusted effect of tube type on cfDNA and FF. RESULTS: We found a positive association between cfDNA quantity and blood shipment time in EDTA tubes, which is significantly reduced with the use of Streck tubes. Furthermore, we show the storage of plasma at -80 °C is associated with a 4.4% annual relative decrease in cfDNA levels. FF was not associated with collection tube type when controlling for confounding variables. However, FF was positively associated with gestational age and trisomy 21, while negatively associated with BMI, male fetus, trisomy 18, Turners syndrome and triploidy. CONCLUSIONS: Preexamination, maternal and fetal variables are associated with cfDNA quantity and FF. The consideration of these variables in future studies may help to reduce the number of pregnant women with inconclusive tests as a result of low FF.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".