Nutrition Modulation of Gastrointestinal Toxicity Related to Cancer Chemotherapy
Bibliographic record
Abstract
Chemotherapy-induced gut toxicity is a major dose-limiting toxicity for many anticancer drugs. Gastrointestinal (GI) complications compromise the efficacy of chemotherapy, promote overall malnutrition, aggravate cancer cachexia, and may contribute to worsened prognosis. The GI tract is an attractive target for nutrition modulation, owing to its direct exposure to the diet, participation in uptake and metabolism of nutrients, high rate of cell turnover, and plasticity to nutrition stimuli. Glutamine, ω-3 polyunsaturated fatty acids, and probiotics/prebiotics are therapeutic factors that potentially modulate GI toxicity related to cancer treatments. Preclinical and clinical evidence are reviewed to critically define plausible benefits of these factors and their potential development into adjuncts to cancer chemotherapy. Mechanisms underlying the action of these nutrients are being unraveled in the laboratory. Optimal strategies to translate these findings into clinical care still remain to be elucidated. Key questions that remain to be answered include the following: which nutrient or combination of nutrients is selected for which patient and chemotherapy regimen? What mechanisms are responsible for modulation, and how are nutrient(s) administered in a clinically optimal manner? Research exploring interactions between different nutrients in GI protection is ongoing and demands further understanding. How nutrition preparations given to chemotherapy-treated patients are formulated in terms of component selection and dose optimization should be carefully studied and justified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 0.001 |
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".