Management of Gastrointestinal and Nutritional Problems in Children With Neurological Impairment
Bibliographic record
Abstract
OBJECTIVES: The main aim of this study was to determine the impact on clinical practice of the first European Society of Gastroenterology, Hepatology, and Nutrition (ESPGHAN) position paper on the diagnosis and management of nutritional and gastrointestinal problems in children with neurological impairment (NI). METHODS: In this pilot-study, a web-based questionnaire was distributed between November, 2019 and June, 2020, amongst ESPGHAN members using the ESPGHAN newsletter. Fifteen questions covered the most relevant aspects on nutritional management and gastrointestinal issues of children with NI. A descriptive analysis of responses was performed. RESULTS: A total of 150 health professionals from 23 countries responded to the survey. A considerable variation in clinical practice concerning many aspects of nutritional and gastrointestinal management of children with NI was observed. The most frequently used method for diagnosing oropharyngeal dysfunction was the direct observation of meals with or without the use of standardised scores (n = 103). Anthropometric measurements were the most commonly used tools for assessing nutritional status (n = 111). The best treatment for gastroesophageal reflux disease (GERD) was considered to be proton pump inhibitor therapy by most (n = 116) participants. Regarding tube feeding, nearly all respondents (n = 114) agreed that gastrostomy is the best enteral access to be used for long-term enteral feeding. Fundoplication was indicated at the time of gastrostomy placement especially in case of uncontrolled GERD. CONCLUSIONS: More studies are required to address open questions on adequate management of children with NI. Identifying knowledge gaps paves the way for developing updated recommendations and improving patient care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".