The evolution of nutrition management in children with severe neurological impairment with a focus on cerebral palsy
Bibliographic record
Abstract
Nutritional management of children with severe neurological impairment (SNI) is highly complex, and the profile of this population is changing. The aim of this narrative review was to give the reader a broad description of evolution of the nutritional management of children with SNI in a high resource setting. In the last decade, there has been an emphasis on using multiple anthropometric measures to monitor nutritional status in children with SNI, and several attempts at standardising the approach have been made. Tools such as the Feeding and Nutrition Screening Tool, the Subjective Global Nutrition Assessment, the Eating and Drinking Ability Classification System and the Focus on Early Eating and Drinking Swallowing (FEEDS) toolkit have become available. There has been an increased understanding of how the gut microbiome influences gastrointestinal symptoms common in children with SNI, and the use of fibre in the management of these has received attention. A new diagnosis, 'gastrointestinal dystonia', has been defined. The increased use and acceptance of blended food tube feeds has been a major development in the nutritional management of children with SNI, with reported benefits in managing gastrointestinal symptoms. New interventions to support eating and drinking skill development in children with SNI show promise. In conclusion, as the life expectancy of people with SNI increases due to advances in medical and nutrition care, our approach necessitates a view to long-term health and quality of life. This involves balancing adequate nutrition to support growth, development and well-being while avoiding overnutrition and its associated detrimental long-term effects.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".