A Global Strategy for Building Clinical Capacity and Advancing Research in the Context of Malnutrition and Cancer in Children within Low- and Middle-Income Countries
Bibliographic record
Abstract
Cancer is one of the prominent noncommunicable diseases and is responsible for more than 8 million deaths each year worldwide. It is expected to impact up to 22 million people annually by 2030, and more than 60% of new patient cases will be in Asia, Africa, and Central and South America. Despite improvements in the delivery of care to children in low- and middle-income countries, survival of those with cancer is as low as 10%; a figure that is in stark contrast to overall childhood cancer survival rates in North America and Western Europe. Although many factors are contributing to this disparity, access to well-educated health-care workers, knowledgeable in both antineoplastic and supportive care, particularly nutritional assessment and therapy, is necessary for effective treatment and reduced morbidities of children with cancer. To this end, we identify approaches for advancing nutritional care such as building nutritional capacity and education as well as advancing rigorous nutritional science through the establishment of multicountry research groups among pediatric oncology units located in low- and middle-income countries.
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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".