Examining guidelines and new evidence in oncology nutrition: a position paper on gaps and opportunities in multimodal approaches to improve patient care
Bibliographic record
Abstract
Malnutrition, muscle loss, and cachexia are prevalent in cancer and remain key challenges in oncology today. These conditions are frequently underrecognized and undertreated and have devastating consequences for patients. Early nutrition screening/assessment and intervention are associated with improved patient outcomes. As a multifaceted disease, cancer requires multimodal care that integrates supportive interventions, specifically nutrition and exercise, to improve nutrient intake, muscle mass, physical functioning, quality of life, and treatment outcomes. An integrated team of healthcare providers that incorporates societies' recommendations into clinical practice can help achieve the best possible outcomes. A multidisciplinary panel of experts in oncology, nutrition, exercise, and medicine participated in a 2-day virtual roundtable in October 2020 to discuss gaps and opportunities in oncology nutrition, alone and in combination with exercise, relative to current evidence and international societies' recommendations. The panel recommended five principles to optimize clinical oncology practice: (1) position oncology nutrition at the center of multidisciplinary care; (2) partner with colleagues and administrators to integrate a nutrition care process into the multidisciplinary cancer care approach; (3) screen all patients for malnutrition risk at diagnosis and regularly throughout treatment; (4) combine exercise and nutrition interventions before (e.g., prehabilitation), during, and after treatment as oncology standard of care to optimize nutrition status and muscle mass; and (5) incorporate a patient-centered approach into multidisciplinary care.
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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 | 0.126 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.026 | 0.030 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".