Enhanced Recovery after Fetal Sequencing: A Perinatal Genomic Scoping Review of Exome/Genome Testing for Reproductive/Obstetric-MFM Providers to Initiate Knowledge Translation following a Screening Ultrasound Identifying Fetal Anomalies
Bibliographic record
Abstract
BACKGROUND: This review of genomic perinatal opportunities and uses will provide counseling and personal genetic knowledge for improved patient care. SUMMARY: This focused systematic analysis and review has used PubMed keywords to identify genomic testing for ultrasound-identified fetal anomaly(ies) that require diagnostic testing after an informed consent process. Multiple fetal anomalies, using TRIO sequencing processes, have a better diagnostic yield, with certain cohorts >50%. For the single anatomic categories, skeletal system, central nervous system, and renal system, using WES fetal sequencing (most commonly) for a diagnostic result, have the larger incremental diagnostic yield over the chromosome micro-array. KEY MESSAGES: The phenotype-genotype (fetal-genomic result) consideration and use of the prenatal exome sequencing technology can be summarized using a SWOT analysis: strength (enhanced evaluation of fetal-neonatal genomic abnormalities not identified by standard chromosomal microarray and improved ethical care decisions); weakness (the understanding and complexity of genomic pathology and testing/the fiscal cost for professional time and the health system services); opportunity (an increased recognition of fetal genetic risk pathology [de novo or inherited carrier mutations] with improved understanding and knowledge translation of counseling for recurrence risk); threat (inability to provide a genetic diagnosis or interpret a variant of unknown significance or the discovery of incidental findings or unanticipated parental genomic diagnoses).
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".