Prenatal Genetic Testing in the Era of Next Generation Sequencing: A One-Center Canadian Experience
Bibliographic record
Abstract
The introduction of next generation sequencing (NGS) technologies has revolutionized the practice of Medical Genetics, and despite initial reticence in its application to prenatal genetics (PG), it is becoming gradually routine, subject to availability. Guidance for the clinical implementation of NGS in PG, in particular whole exome sequencing (ES), has been provided by several professional societies with multiple clinical studies quoting a wide range of testing yields. ES was introduced in our tertiary care center in 2017; however, its use in relation to prenatally assessed cases has been limited to the postnatal period. In this study, we review our approach to prenatal testing including the use of microarray (CMA), and NGS technology (gene panels, ES) over a period of three years. The overall diagnostic yield was 30.4%, with 43.2% of those diagnoses being obtained through CMA, and the majority by using NGS technology (42% through gene panels and 16.6% by ES testing, respectively). Of these, 43.4% of the diagnoses were obtained during ongoing pregnancies. Seventy percent of the abnormal pregnancies tested went undiagnosed. We are providing a contemporary, one tertiary care center retrospective view of a real-life PG practice in the context of an evolving use of NGS within a Canadian public health care system that may apply to many similar jurisdictions around the world.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".