P-07: Vascular Anomalies Associated With Esophageal Atresia and Tracheoesophageal Fistula: Incidence, Clinical Presentation, Diagnosis and Consequences
Bibliographic record
Abstract
Vascular anomalies may be associated with esophageal atresia (EA) and tracheoesophageal fistula (TEF). Our main objective is to report their incidence in a cohort of EA/TEF patients while describing clinical presentation, diagnosis and consequences. The secondary objective is to determine the diagnostic value of esophagram in the diagnosis of aberrant right subclavian artery (ARSA). All patients born with EA/TEF from 2005 to 2013 were studied. Preoperative echocardiography reports, surgical description of primary esophageal repair and esophagram were retrospectively reviewed. Age at diagnosis, discovery mode, clinical presentation and need for surgical correction of the vascular malformation were noted. 76 of 86 children with EA/TEF were included. Fourteen children (18%) had a vascular malformation. The incidence of right aortic arch (RAA) and ARSA was 6% (5/76) and 12% (9/76) respectively. Respiratory and/or digestive symptoms occurred in 9 of them. Long gap EA and severe cardiac malformations requiring surgery were both significantly associated with vascular anomalies (p < 0.05). We reviewed 254 esophagrams; 40% were inconclusive for the detection of vascular anomalies. The diagnosis of vascular malformation was missed in four patients with a long gap EA. The sensitivity of esophagram for the diagnosis of ARSA was 66%, the specificity was 98%, the negative predictive value 95%, and the positive predictive value 85%. ARSA and RAA have an incidence respectively of 12% and 6% in EA/TEF patients. Echocardiography and esophagram are effective but their sensitivity is not optimal for the diagnosis of ARSA. A CT-angioscan is recommended when esophageal stenting is indicated.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".