Meeting Minimum ESPEN Energy Recommendations Is Not Enough to Maintain Muscle Mass in Head and Neck Cancer Patients
Bibliographic record
Abstract
The relationship between dietary intake and body composition changes during cancer treatment has not been well characterized. The aim of this study was to compare dietary intake at diagnosis and end of treatment in relation to changes in muscle mass and adiposity in head and neck cancer (HNC) patients. Dietary intakes (three-day food record) and body composition using computed tomography (CT) were assessed at diagnosis (baseline) and after treatment completion (post-treatment). Skeletal muscle (SM) loss was explored as a consequence of energy and protein intake in relation to the minimum and maximum European Society of Parenteral and Enteral Nutrition (ESPEN) guidelines. Higher energy intakes (kcal/kg/day) and increases in energy intake (%) from baseline to post-treatment were correlated with attenuated muscle loss (r = 0.62, p < 0.01; r = 0.47, p = 0.04, respectively). Post-treatment protein intake demonstrated a weak positive correlation (r = 0.44, p = 0.05) with muscle loss, which did not persist when controlling for covariates. Meeting minimum ESPEN energy guidelines (25 kcal/kg/day) did not attenuate SM loss, whereas intakes >30 kcal/kg/day resulted in fewer participants losing muscle. Greater baseline adiposity correlated with greater SM loss (p < 0.001). Energy intakes of 30 kcal/kg/day may be required to protect against SM loss during treatment in HNC patients. The influence of adiposity on SM loss requires further exploration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".