Use of Clinically Informed Strategies and Diagnostic Yields of Genetic Testing for Fetal Structural Anomalies Following a Non‐Diagnostic Microarray Result: A Population‐Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To investigate the performance of targeted gene sequencing, expanded gene panels, and selected exomes for prenatally identified fetal anomalies, after non-diagnostic microarray results. METHOD: All fetal samples received for genetic testing for fetal structural anomalies in the Canadian Maritime Provinces (2014-2022) were identified. Utilization and results of NGS sequencing strategies after a non-diagnostic microarray were correlated with ultrasound findings and autopsy results. RESULTS: Five hundred and ninety-three cases of fetal anomalies with non-diagnostic RAD results were identified, including 319 (54%) with isolated anomalies. Diagnostic yield from the microarray was 7.5%. Sequence-based testing for 131 cases gave an overall diagnostic yield of 38% (8.4% of initial cohort). For isolated anomalies, diagnostic yield was highest in the intracranial, renal, and musculoskeletal systems (44%, 60%, 64% respectively). Appropriate targeted gene sequencing provided a diagnostic yield of 40%. With clinically indicated criteria for exome analysis, diagnostic yields were higher than when clinical information prompted use of a selected gene panel (73% vs. 27%). Expanding to an exome after a non-diagnostic gene panel had an additional diagnostic yield of 13%. CONCLUSION: Multidisciplinary review and comprehensive clinical information can inform the selection of strategies for expanded genetic testing after non-diagnostic microarray for fetal anomalies within a publicly funded health care system.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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; 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".