Nutritional status of children during treatment for acute lymphoblastic leukemia in Guatemala
Bibliographic record
Abstract
BACKGROUND: Most children with cancer live in developing countries where the prevalence of malnutrition may reach 50% and influence the course of the disease. This study examined the prevalence and severity of malnutrition at diagnosis, as well as after 3 and 6 months of chemotherapy, in children with acute lymphoblastic leukemia (ALL) in Guatemala. METHODS: Triceps skin fold thickness (TSFT) and mid upper arm circumference (MUAC) provided measures of nutritional status (NS) in three categories: adequately nourished (A): TSFT and MUAC > 10th percentile; severely depleted (SD): TSFT or MUAC < 5th percentile; and moderately depleted (MD): all the remaining patients. RESULTS: Of 331 new patients, 241 had NS assessed at diagnosis. A = 113 (46.9%); MD = 28 (11.6%); SD = 100 (41.5%). At 3 months A = 106 (52.2%); MD = 25 (12.3%); SD = 72 (35.5%). At 6 months A = 146 (76.0%); MD = 12 (6.3%); SD = 34 (17.7%). In multivariate analysis, SD children at 6 months of treatment had a hazard of death that was 2.4-fold the hazard of those A or MD (95% CI: 1.3-4.7) CONCLUSIONS: Malnutrition is prevalent in newly diagnosed children with ALL in Guatemala and severe nutritional depletion is apparently predictive of abandonment of therapy and relapse of disease, but if children survive and improve their NS in the first 6 months after diagnosis, their chances of survival may improve significantly to approximate those in children not presenting with nutritional depletion.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".