Pediatric oncology nutritional practices in high‐income countries: A survey from the International Society of Paediatric Oncology (SIOP)
Bibliographic record
Abstract
BACKGROUND: Optimal nutrition in pediatric oncology can influence cancer-related outcomes. To establish an understanding of nutrition practice and perceptions of best practice, we queried nutrition providers practicing in pediatric oncology care centers in high-income countries. METHODS: An electronic, multidisciplinary, cross-sectional survey of nutrition practices was conducted among pediatric oncology nutrition practitioners. Final analysis included 110 surveys from 71 unique institutions and included practitioners from Europe, the United States, Canada, Australia/New Zealand, South America, and the Middle East/Asia. RESULTS: The majority of institutions (97%) reported having dietitians; 72% had designated oncology dietitians. Approximately half of the practitioners (47%) reported feeling their institutions were inadequately staffed. The majority (78%) of institutions completed nutrition risk screening, but there was no consensus on specific screening practices. Half (50%) of the institutions that screened for nutrition risk did so in both inpatient and outpatient settings. The majority (80%) of institutions completed a nutrition assessment close to the time of diagnosis. Those that did not cite lack of staff and/or lack of time, lack of standardized approach, and consult only level of nutritional care as primary barriers. The most common topic of nutrition education provided to patients/families was nutrition-related symptom management (68%). CONCLUSION: While most institutions reported having pediatric oncology dietitians, we found a lack of standardized practice and perceived inadequate staffing. In addition, what providers perceived to be best practice did not always align with day-to-day clinical practice. Ongoing efforts are needed to develop evidence-based guidelines, including staffing recommendations, to support specialized care in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".