MG-128 Use of prenatal array comparative genomic hybridization in cases of fetal structural cardiac anomalies: New cases and review of the literature
Bibliographic record
Abstract
Background Array comparative genomic hybridization (aCGH) has been used to provide genome-wide screening for small chromosome imbalances in the prenatal setting, however use is not uniform across Canada. Many studies have looked at overall yield of aCGH, however, there has been less literature examining utility of array in the case of specific congenital anomalies. Objectives To determine the utility of aCGH in cases of prenatal cardiac anomalies. Methods A literature review was conducted using PubMed for all studies reporting results from prenatal aCGH, and those reporting cardiac anomalies as a distinct category were selected. Results of aCGH testing for cases prospectively recruited for this indication at our centre were also included. Outcome measures included detection rate, number of variants of uncertain significance (VOUS), and number of incidental findings. Results Eleven published studies and 22 patients at our centre were included. Most studies did not report cardiac anomaly-specific results for all categories of array results. Overall detection rate over karyotype for pathogenic anomalies was 6.6%. Incidental findings were found in 7.69% of cases. VOUS occurred in 1.47% of cases. Conclusions Array CGH increases the yield of chromosomal findings over karyotype alone in cases of prenatal cardiac anomalies, and has a place in clinical use. In addition, VOUS and incidental findings are as common as pathogenic anomalies in this cohort. Prenatal clinics must be prepared to deal with these findings in this setting. More studies are needed to determine the incidence of pathogenic, VOUS and incidental findings in cardiac-specific cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".