Assessment and clinical utility of a non-Next-Generation Sequencing based Non-Invasive Prenatal Testing technology
Bibliographic record
Abstract
Abstract Background Rolling circle replication (RCR) is a novel technology that has not been applied to cell-free DNA (cfDNA) testing until recently. Given the cost and simplicity advantages of this technology compared to other platforms currently used in cfDNA analysis, an assessment of RCR in clinical laboratories was performed. Here, we present the first validation study from clinical laboratories utilizing RCR technology. Methods 831 samples from spontaneously pregnant women carrying a singleton fetus and 25 synthetic samples were analyzed for the fetal risk of Trisomy 21, Trisomy 18 and Trisomy 13 by three laboratories on three continents. All women who provided the samples were followed to birth, where evaluation for fetal aneuploidies was performed using newborn examinations and any suspected aneuploidies were confirmed with karyotyping. Results The study found rolling circle replication to be a highly viable technology for clinical assessment of fetal aneuploidies with 100% sensitivity for T21 (95% CI:82.35% - 100.00%); 100.00% sensitivity for T18 (71.51% - 100.00%) and 100.00% sensitivity for T13 analyses (66.37% - 100.00%). The specificities were >99% for each trisomies [99.7% (99.01% - 99.97%) for T21; 99.5% (98.62% - 99.85%) for T18; 99.7% (99.03% - 99.97%) for T13], along with a first pass no-call rate of 0.93%. Conclusions The study showed that using a rolling circle replication-based cfDNA system for the evaluation of the common aneuploidies would provide greater accuracy and clinical utility compared to conventional biochemical screening and comparable results to other reported cfDNA methodologies.
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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 | 0.007 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".