Genetic Testing in a Cohort of Complex Esophageal Atresia
Bibliographic record
Abstract
The objective of the present study is to describe a cohort of complex esophageal atresia and the yield of genetic tests performed for such patients. We selected 45 patients with complex esophageal atresia (EA), namely those having at least one associated anomaly. We reviewed their medical records to assess clinical features, other diagnoses, and genetic investigations. Most of the patients had a diagnosis of VACTERL association (56%) with no genetic variant identified. Interestingly, 5 patients in the cohort (11%) had a right pulmonary hypoplasia or agenesis. A majority of our cohort (73%) had genetic testing; 60% were karyotyped (abnormal in 4 of the 27 patients tested), 31% had aCGH (abnormal in 1 of the 14 patients tested), and 31% had diepoxybutane (DEB) testing for Fanconi anemia (abnormal in 2 of the 14 patients tested). One patient had exome sequencing studies, but no candidate gene was identified. Various anomalies were associated with EA, and overall a genetic variant could be identified in 7 of the 33 patients tested. Chromosomal studies such as aCGH and chromosomal breakage studies should be considered, and their yield varied between 7 and 14%. Other genetic investigations such as exome sequencing could possibly have even higher yields but will need to be assessed in a large cohort. Improved genetic diagnoses in EA may improve the management of these patients by directing specific surveillance and management schemes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".