Implemented nutritional intervention algorithm in pediatric oncology compared to standard nutritional supportive care outcomes
Bibliographic record
Abstract
AIM: To implement a childhood cancer-specific nutritional algorithm adapted for the South African context for interventions at time-set intervals to evaluate differences in the nutritional status of newly diagnosed children with cancer. METHOD: Children with newly diagnosed cancer were assessed for stunting, underweight, wasting, and moderate to severe malnutrition (MUAC < -2SD and < - 3 SD) between October 2018 and December 2020 in a longitudinal nutritional assessment study with monthly assessments. Two pediatric oncology units (POUs) served as the intervention group that implemented the nutritional algorithm-directed intervention and three other POUs formed the control group that implemented standard supportive nutritional care. RESULTS: A total of 320 patients were enrolled with a median age of 6.1 years (range three months to 15.3 years) and a male-to-female ratio of 1.1:1. The malnourished patients in the intervention group showed significant improvement at six months after diagnosis for stunting (P = 0.028), underweight (P < 0.001), and wasting until month five (P = 0.014). The improvements in the control group were not significant. Moderate acute malnutrition (MAM) significantly improved over the first six months of cancer treatment in the intervention group (P < 0.001), while MAM improvement was only significant in the control group for the children under five years of age (P = 0.004). The difference in mean z-scores over time for the nutritional parameters between the intervention and control groups was insignificant. CONCLUSION: We established that the nutritional algorithm adapted for South Africa as an intervention tool for childhood cancer assisted in optimizing nutritional interventions and improved nutritional outcomes over the first six months of 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".