Management of Gastroesophageal Reflux Disease in Esophageal Atresia Patients
Bibliographic record
Abstract
OBJECTIVES: After surgical repair, up to 70% of esophageal atresia (EA) patients suffer from gastroesophageal reflux disease (GERD). The ESPGHAN/NASPGHAN guidelines on management of gastrointestinal complications in EA patients were published in 2016. Yet, the implementation of recommendations on GERD management remains poor.We aimed to assess GERD management in EA patients in more detail, to identify management inconsistencies, gaps in current knowledge, and future directions for research. METHODS: A digital questionnaire on GERD management in EA patients was sent to all members of the ESPGHAN EA working group and members of the International network of esophageal atresia (INoEA). RESULTS: Forty responses were received. Thirty-five (87.5%) clinicians routinely prescribed acid suppressive therapy for 1-24 (median 12) months. A fundoplication was considered by 90.0% of clinicians in case of refractory GERD with persistent symptoms despite maximal acid suppressive therapy and in 92.5% of clinicians in case of GERD with presence of esophagitis on EGD. Half of clinicians referred patients with recurrent strictures or dependence on transpyloric feeds. Up to 25.0% of clinicians also referred all long-gap EA patients for fundoplication, those with long-term need of acid suppressants, recurrent chest infections and feedings difficulties. CONCLUSIONS: Respondents' opinions on the optimal duration for routine acid suppressive therapy and indications for fundoplication in EA patients varied widely. To improve evidence-based care for EA patients, future prospective multicenter outcome studies should compare different diagnostic and treatment regimes for GERD in patients with EA. Complications of therapy should be one of the main outcome measures in such trials.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".