Efficacy of ready‐to‐use therapeutic food in malnourished children with cancer: Results of a randomized, open‐label phase 3 trial
Bibliographic record
Abstract
BACKGROUND: The adverse influence of undernutrition in children with cancer may be remediated by early nutritional intervention. This study assessed the efficacy of ready-to-use therapeutic food (RUTF) in improving nutritional status and reducing treatment-related toxicities (TRTs) in such children. METHODS: In a randomized controlled phase-3 open-label trial, severely and moderately undernourished children with cancer were randomized 1:1 to receive standard nutritional therapy (SNT) or SNT+RUTF for 6 weeks. The primary outcome (weight gain >10%) and secondary outcomes (improved/maintained nutritional status, improved body composition) were assessed after 6 weeks. TRTs were assessed over 6 months. RESULTS: Between July 2015 and March 2018, 260 subjects were enrolled, 126 were analyzable in both arms at 6 weeks. More children on RUTF had weight gain (98 [77.8%] vs. 81 [64.2%], p = .025) with a greater increase in fat mass as a percentage of body mass (median 2% [IQR -0.12 to 4.9] vs. 0.5% [IQR -1.45 to 2.27, p = .005]) but a greater loss of lean mass (median -1.86% [IQR -4.4 to 0.50] vs. -0.4% [IQR -2.4 to 1.4, p = .007]) compared to the SNT arm. Fewer subjects on the RUTF arm had episodes of severe infection (10.6% vs. 31%, p < .0001), treatment delays (17.7% vs. 39%, p < .0001), and severe mucositis (11% vs. 23.8%, p = .006) compared to the SNT arm. The odds of developing TRTs on the RUTF arm were lower even after adjusting for improvement in nutritional status. CONCLUSIONS: RUTF is efficacious in improving weight gain and nutritional status in undernourished children with cancer and decreases TRTs. Incorporating RUTF into a healthy, balanced diet should be considered in undernourished children with cancer.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".