Prevalence of Chronic and Acute Malnutrition and Association With Overall Three‐Year Survival in Newly Diagnosed Children With Cancer in South Africa
Bibliographic record
Abstract
INTRODUCTION: This study investigated the prevalence of malnutrition at childhood cancer diagnosis in South Africa and the association with 1-year post-diagnosis overall survival (OS). METHOD: Nutritional status was prospectively assessed for newly diagnosed children with cancer. Chronic undernutrition was defined as two standard deviations (SDs) or more below zero for height/length-for-age (HAZ), and acute as underweight (weight-for-age [WAZ], and wasted as body mass index for age [BAZ] and mid-upper arm circumference for age [MUAC/A]). The association between the nutritional status at diagnosis and age, sex, disease group and 1-year post-diagnosis OS was analysed with Cox regression and hazard ratios (HRs). RESULTS: Less than 15% were chronically malnourished (stunted: 14.3%) and up to 24.3% acutely undernourished (wasted: 24.3% MUAC-Z and BAZ 8.1%), 11.6% underweight, of 320 patients at cancer diagnosis). More females than males were underweight (12.2% vs. 4.5%; p = 0.027). Children of 5 years of age and older had a higher prevalence of wasting (18.7%) than children under 5 years of age (3.9%) (p < 0.001) at diagnosis, with significant improvement 6 months after diagnosis. Stunting was significantly associated with poorer OS at 3 years after a cancer diagnosis (HR 1.8; 95% CI 1.1, 2.8; p = 0.011). CONCLUSION: MUAC/A identified more children with undernutrition than other nutritional parameters. Stunting was significantly associated with poorer OS 3 years and EFS 2 years after a cancer diagnosis. Optimal nutritional support should be provided for South African children, especially those with acute and chronic malnutrition, to improve OS.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".