Nutrition of Children With Cancer in Brazil: A Systematic Review
Bibliographic record
Abstract
PURPOSE: The objective of this systematic review was to describe nutrition-related publications on children and adolescents diagnosed with cancer in Brazil. METHODS: The methodology followed that of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Medline, LILACS (the Latin American & Caribbean Health Sciences Literature), and Embase were searched in April 2019, and data extraction and rating of methodologic study quality (according to the National Institutes of Health quality score assessment) were performed independently by reviewers. RESULTS: Twenty-seven studies met the inclusion criteria, reporting on 3,509 patients from 1994 to 2018. Most of the studies (74%) were of poor quality in methodology and reporting. Different cancer diagnoses were included in 52% of studies, whereas acute leukemia was the exclusive focus in 41%. The majority of the articles (70%) were from institutions in the Southeast Region of Brazil, mainly the state of São Paulo (74%); no publications were from the North Region of the country. Twelve studies addressed nutritional status and body composition, reporting an abundance of malnourished patients in the Brazilian population of children and adolescents with cancer. Six studies on micronutrients pointed to possible deficiencies in this population, with a yet unclear but promising role for supplementation during treatment. CONCLUSION: Evidence indicates that there is great interest in the impact of nutrition on childhood cancer treatment and clinical outcomes in Brazil. However, there is a need to focus on high-quality research, particularly with multicentric/national studies. This will help establish research priorities and better planned clinical interventions, adapted to each region of the country.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".