Non-Communicable Diseases in Children: Systems-Based Approaches to Incorporating Nutrition into Medical Care
Bibliographic record
Abstract
Non-communicable diseases (NCDs) affect over 2.1 billion children globally, accounting for 15.9% of deaths in children under 20 and contributing 174 million years lived with disability. Integrating nutrition care into NCD management within health systems can save lives, reduce costs, and improve quality of life. Nutrition interventions have been found to improve survival rates in children with cancer by 30%. Incorporating early nutrition interventions in hospitals is associated with a 36% reduction in per-patient costs. Despite these clear benefits, nutrition care is often not readily accessible as part of NCD management in children. Access to trained nutrition professionals is limited, and nutrition training for healthcare workers is often inadequate. There are cost-effective and scalable models for delivering high-quality nutrition care, but scaling these models will require commitment to capacity building, training, technological innovation, and monitoring frameworks. Coordinated, multisectoral responses are needed urgently to incorporate nutrition sustainably into healthcare systems to confront the growing burden of childhood NCDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".