Nutritional assessments and interventions in head and neck, esophageal, rectal and lung cancers undergoing anticancer treatments: a literature review
Bibliographic record
Abstract
In the last decades, greater toxicities deriving from anticancer treatments (especially chemo and/or radiotherapy), along with cancer sites, increase the risk of nutritional status impairment. This condition should be avoided because it could determine early therapies' interruptions and worse clinical outcomes. In this review, we aim to provide an overview of the current evidence obtained from Pubmed and Embase databases assessing the role of nutritional assessments and interventions during anticancer treatments, with a particular focus on immunonutrition. Actual evidence suggests that nutritional practices are different worldwide, however, it is essential to define an adequate and standardized nutritional evaluation including at least food intake estimation, anthropometric measurements and body composition analysis. Nutritional interventions should always include intensive counseling and, in some cases, the prescription of specific dietary supplements. Nowadays, immunonutrition formulas represent a promising tool to improve many nutritional and treatment outcomes, but further studies are still necessary to define an evidence based clinical practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".