Cancer and nutrition among children and adolescents in low- and middle-income countries
Bibliographic record
Abstract
OBJECTIVE: : The primary purpose of this review is to investigate the relationships between cancer and nutrition in children and adolescents living in resource-poor, low- and middle-income countries (LMICs) in order to explore potential opportunities for interventions which could improve clinical outcomes in this population. METHOD: : The implications of overlapping age groups of children and adolescents with cancer are examined, as are the adverse influences of under-nutrition and socio-economic deprivation on the efficacy of treatment and cancer survival. Evidence suggestive of novel avenues to enhance prospects for cure, based on increased understanding of the dynamic of nutrition and cancer, is evaluated. RESULT: : Cancer adds to the burden of under-nutrition in disadvantaged populations which is reflective, in large measure, on inadequate governmental expenditure on health which demands public-private partnerships and the use of hospital-based foundations. Structured approaches to the measurement of nutritional status and the design of effective programmes of nutritional supplementation are of proven benefit. Initial results from studies of the perturbed gut microbiome hold considerable promise for further gains. CONCLUSION: A large minority of children with cancer in LMICs are never diagnosed and the same may be true of adolescents. Investing in the training of nutritionists will have substantial returns as will increasing access to essential medicines. Recognition of these challenges has stimulated WHO and other entities to devise major strategies for comprehensive changes in the care of children and adolescents with cancer in LMICs, offering realistic prospects for improved clinical outcomes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".