Validation of an algorithmic nutritional approach in children undergoing chemotherapy for cancer
Bibliographic record
Abstract
BACKGROUND: Undernutrition impacts clinical outcome adversely in children with cancer. This study aimed to validate a nutritional algorithm with specific application to the low- and middle-income country (LMIC) setting. PROCEDURE: Fifty children with a new diagnosis of cancer were enrolled in this randomized interventional study. Weight, height/length, and mid-upper-arm circumference (MUAC) were measured at baseline. The study arm was administered nutritional care as per the algorithm and the control arm received the institutional standard of care. Weight was monitored regularly and MUAC was repeated after 3 months. Children were classified based on weight for height if <2 years of age or body mass index if ≥2 years, as normal, wasted, and severely wasted. The algorithmic approach comprised administration of oral supplements, nasogastric feeds, and/or parenteral nutrition based on objective assessment of the nutritional status. RESULTS: Fifty patients were analyzed (study: 25, control: 25). Four in the study arm (16%) and six in the control arm (24%) had wasting at baseline. MUAC was <5th percentile in 15 (60%) and 13 (52%) patients in the study and control arms, respectively. At the end of 3 months, the median increment in weight was 0.8 kg (interquartile range [IQR]: -0.02; 2.00) and 0.0 kg (IQR: -0.70; 1.25) in the study and control arms, respectively (P = .153). The median increment in MUAC was 1.20 cm (IQR: 0.10; 2.30) and 0.00 cm (IQR: -0.50; 1.10) in the study and control arms, respectively (P = .020). CONCLUSIONS: The application of an algorithm designed for use in LMICs resulted in significant improvement in nutritional status, as measured by MUAC.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".