Prevalence of Poverty and Hunger at Cancer Diagnosis and Its Association with Malnutrition and Overall Survival in South Africa
Bibliographic record
Abstract
Many South African children live in poverty and food insecurity; therefore, malnutrition within the context of childhood cancer should be examined. Parents/caregivers completed the Poverty-Assessment Tool (divided into poverty risk groups) and the Household Hunger Scale questionnaire in five pediatric oncology units. Height, weight, and mid-upper arm circumference assessments classified malnutrition. Regression analysis evaluated the association of poverty and food insecurity with nutritional status, abandonment of treatment, and one-year overall survival (OS). Nearly a third (27.8%) of 320 patients had a high poverty risk, associated significantly with stunting (p = 0.009), food insecurity (p < 0.001) and residential province (p < 0.001) (multinomial regression). Stunting was independently and significantly associated with one-year OS on univariate analysis. The hunger scale was significant predictor of OS, as patients living with hunger at home had an increased odds ratio for treatment abandonment (OR 4.5; 95% CI 1.0; 19.4; p = 0.045) and hazard for death (HR 3.2; 95% CI 1.02, 9.9; p = 0.046) compared to those with food security. Evaluating sociodemographic factors such as poverty and food insecurity at diagnosis is essential among South African children to identify at-risk children and implement adequate nutritional support during cancer treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".