Effective nutritional intervention improves the health-related quality of life (HRQL) of undernourished children with cancer: results of a randomized, controlled phase 3 trial
Bibliographic record
Abstract
PURPOSE: The association of nutritional intervention and health-related quality of life (HRQL) was examined in children with cancer. METHODS: Undernourished children with cancer (N = 260) were randomized 1:1 to standard nutritional therapy (SNT) or SNT + Ready to Use Therapeutic Food (RUTF). HRQL was assessed using the Health Utilities Index® (HUI) at study entry and 6 weeks later. HUI has two systems, HUI2 and HUI3, with derived categories of disability. Scores after SNT and SNT + RUTF were compared. RESULTS: At study entry there were no significant differences in HRQL scores between the two treatment arms. After 6 weeks of nutritional intervention, median overall HUI2 scores were comparable-0.90 with RUTF and 0.89 with SNT (p = 0.317)-but overall HUI3 scores were 0.90 and 0.79 (p = 0.009), respectively. At study entry and 6 weeks, 87 (35%) and 116 (46.6%) subjects had no or mild disability, and 162 (65%) and 133 (53.4%) had moderate or severe disability (p < 0.001) respectively using HUI2 scores. Corresponding scores with HUI3 were 66 (26.5%) and 103 (41.3%) for no or mild disability and 183 (73.5%) and 146 (58.6%) (p < 0.001) for moderate or severe disability at study entry and 6 weeks, respectively. Children with weight gain >10% had significant improvement in overall HUI2 (0.92 vs 0.84; p = 0.049) and HUI3 scores (0.88 vs 0.78; p = 0.010). CONCLUSION: In severely and moderately malnourished children with cancer, administration of RUTF and weight gain were associated with improved HRQL. Weight gain can improve HRQL in numerous domains and overall.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".