Bibliographic record
Abstract
CONTEXT: Malnutrition is prevalent in children with cancer at diagnosis, especially in low- and middle-income countries (LMIC) where the great majority of children live. It is associated with an added burden of morbidity and mortality. AIMS: Answers were sought to the best measure of nutritional status in LMIC, the impact of anti-neoplastic therapy, effective interventions to achieve normal nutritional status and the impact of these on clinical outcomes. RESULTS: Arm anthropometry offers reasonable estimates of fat mass and lean body mass that are both impacted adversely by treatment. Nutritional supplementation, including the use of simple local resources, is beneficial and can improve survival. Long-term survivors may continue to exhibit perturbed nutritional status. CONCLUSIONS: The prevalence and severity of malnutrition in children with cancer in LMIC demand attention. Opportunities exist to conduct studies in India to examine the effects of nutritional interventions, including on the overall well-being of survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".