Feeding difficulties, food intake, and growth in children with esophageal atresia
Bibliographic record
Abstract
Abstract Objectives Challenges regarding feeding difficulties and nutrition in children with esophageal atresia (EA) have been sparsely studied. The aim of this study was to explore parent‐reported feeding difficulties in children with EA by applying Montreal Children's Hospital‐Feeding Scale (MCH‐FS), and to further explore associations between feeding difficulties and clinical factors, growth and nutritional intake. Methods Parents of EA children born between 2012 and 2017 were invited. Clinical data were collected from medical records. In a prospective cohort‐study parent‐reported feeding difficulties (by MCH‐FS) were reported at two assessments, and at the second assessment, dietary data were collected by using the 24‐h food‐recall method. Results Out of 55 eligible participants, we evaluated 53 children at median age of 1.6 years (Q1:Q3 1.0:2.9) (first assessment) and 38 at median age of 4.2 years (Q1:Q3 1.0:2.9) (second assessment). Feeding difficulties were reported by 34% and 31% of the parents, respectively, but no particular profile of concerns could be identified. Children's energy intake and weight‐for‐age were correlated with feeding difficulties (MCH‐FS total score) (p < 0.02). Conclusion Parent‐reported feeding difficulties were identified in one‐third of children with EA and related to low energy intake and low weight‐for‐age, but not to clinical factors. This implies that feeding difficulties must be screened for during follow‐up in all EA children and may facilitate early detection of challenges and intervention if needed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".