P-08: Multi-Disciplinary Approach to Transitioning to Oral Feeds Following Placement of Enterostomy Tubes in Infants Post Repair of Esophageal Atresia/Tracheoesophageal Fistula
Bibliographic record
Abstract
Oral feeding post esophageal atresia/tracheoesophageal fistula (EA/TEF) repair can be challenging and require nutrition support via enterostomy tubes (ET) due to strictures, dysphagia and gastroesophogeal reflux disease (GERD). This review examines the multi-disciplinary (MultiD) approach used in our institution. Jan 2011-Apr 2014: 15/36 (42%) EA/TEF repairs required ET, 93% required exclusive ET feeds at discharge. 26% type A; 7% type B; & 67% type C. The MultiD team (occupational therapist, registered dietitian, nurse practitioner & physician) collaborate to support the progression of oral intake through frequent assessments of esophageal patency, oromotor ability, swallowing safety, adequacy of GERD management, & nutritional intervention. Some patients continue to show limited interest in oral intake, hence emphasis is placed on positive oral experiences instead of volume consumed. This helps to establish trust around feeding, which is essential when encouraging progression of oral intake. Family meal times are encouraged to facilitate modeling behaviour and to allow child-led food exploration. When oral liquids are refused, the introduction of developmentally appropriate solid food helps to facilitate oral intake. Throughout, ET feeds are manipulated to support growth while helping to drive hunger/satiety. A MultiD approach helps to manage the complex factors that hinder exclusive oral feeding in the EA/TEF population by drawing on diverse professional expertise to support a positive feeding environment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".