P-01: Progression Toward Oral Feeding in A Child With Long Gap Ea: A Case Presentation of an Interdisciplinary Approach
Bibliographic record
Abstract
Long gap Esophageal Atresia (EA) poses an immediate challenge not only for the surgical and medial teams but to the non-medical team. Aside from possible other malformations or syndromes, long-gap EA signals an immediate and longer term consequence of an inability to feed, which interrupts maternal expectations of nurturing her infant. Repeated surgeries, hospitalizations, dilatations and multiple clinic visits, and the keeping up with multiple medications tend to insidiously wear down maternal energies, and slowly an impatient desire to have the child eat and drink becomes the focus of maternal attention and energies. Currently there is no universally established protocol for introduction of oral feeding. Each centre develops their own protocol for ensuring eventual oral feeding and drinking skills. In our centre, both the nutritionist and occupational therapist are involved early in the neonatal period to evaluate oral motor skills and to manage exclusive gastrostomy and or jujonostomy. When appetitive issues become apparent, or when mothers become weary of their child's slow progress in obtaining full oral feeding, our feeding psychologist becomes involved. A case presentation of a child with long-gap EA will highlight the protocol used for stabilising family expectations and establishing full oral feeding by an interdisciplinary team. Total oral feeding was achieved rapidly by 19 months of age. The case presentation highlights the importance of a guideline for the introduction of oral feedings in the context of an interdisciplinary team approach to long gap EA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| 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".