Impact of variation in practice in the prenatal reporting of variants of uncertain significance by commercial laboratories: Need for greater adherence to published guidelines
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of implementing commercial whole exome sequencing (WES) and targeted gene panel testing in pregnancies with fetal anomalies. METHODS: A retrospective chart review of 124 patients with sequencing performed by commercial laboratories. RESULTS: The diagnostic yield of WES and panel testing was 21.5% and 26%, respectively, based on likely pathogenic (LP) or pathogenic (P) variants. Forty-two percent of exomes and 32% of panels analysed had one or more variants of uncertain significance (VUS) reported. A multidisciplinary in-depth review of the fetal phenotype, disease phenotype, variant data, and, in some patients, additional prenatal or postnatal investigations increased the diagnostic yield by 5% for exome analysis and 6% for panel analysis. CONCLUSIONS: The diagnostic yield of WES and panel testing combined was 23% based on LP and P variants. Although the reporting of VUS contributed to a 5% increase in diagnostic yield for WES and 6% for panels, the large number of VUS reported by commercial laboratories has significant resource implications. Our results support the need for greater adherence to the recommendations on the prenatal reporting of VUS and the importance of a multidisciplinary approach that brings together clinical and laboratory expertise in prenatal genetics and genomics.
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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 | 0.242 | 0.489 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".