The Nutritional Burden of Cancer: Nutritional Status and Body Composition Differences Between Children with Acute Lymphoblastic Leukemia and their Siblings in Guatemala
Bibliographic record
Abstract
During treatment, children with acute lymphoblastic leukemia (ALL) gain fat mass and lose skeletal muscle mass. The great majority live in low- and middle-income countries with few studies of their body composition and none addressing the hypothesis that the disease itself contributes to nutritional morbidity. At diagnosis, children with ALL were compared to their siblings on socioeconomic status (SES). Nutritional status was assessed by mid-upper arm circumference (MUAC)-for-age Z scores and body composition by dual energy x-ray absorptiometry (DXA). Median SES scores for the patients (47.5) and their siblings (47.0) were very similar (P = 0.5). MUAC Z scores for patients aged >5 years were lower than for siblings (P < 0.001). On DXA siblings had a higher mean appendicular lean mass index Z score, a surrogate of skeletal muscle mass, than patients (P = 0.019). A logistic model to estimate the odds ratio (OR) of being severely/moderately under-nourished (classified by MUAC Z score) by SES revealed that, compared with siblings (n = 49), children with ALL (n = 60) had a higher probability of being under-nourished (OR 5.25, 95% CI 1.44–25.95, P = 0.02). The results support the hypothesis that children at diagnosis of ALL in Guatemala are more nutritionally depleted than their apparently healthy siblings.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".