Nutritional Counseling During Chemotherapy Treatment: A Systematic Review of Feasibility, Safety, and Efficacy
Bibliographic record
Abstract
Dietary interventions during chemotherapy hold promise for clinical and supportive care outcomes. We systematically investigated the feasibility, safety, and efficacy of nutritional counseling conducted during chemotherapy. Studies prospectively implemented nutrition counseling during chemotherapy. Articles were identified from three databases—EMBASE, Cochrane Library, and SCOPUS—from inception to 1 October 2024. Feasibility, safety, and efficacy of outcome data were extracted. Among 44 publications, 39 studies recruited 98 ± 80 participants (range 15–360); 38/39 (97%) were randomized controlled trials. One-third (31%) were among patients with breast cancer. Interventions were divided into individualized nutritional counseling (n = 21), nutrition counseling plus exercise (n = 13), and nutrient-specific dietary patterns (n = 10). Many had goals to achieve established nutrition guidelines. Feasibility was high based on attendance at counseling sessions, retention, and/or food log analysis. Overall, there were minimal adverse events related to the interventions. Many studies showed between-group differences favoring the intervention group for body weight (8/24, gain or loss, according to goals), nutritional status (8/9), quality of life (3/10 without and 6/9 with exercise), cancer-related fatigue (7/10), chemotherapy tolerance (6/11), and treatment responses (3/13). In conclusion, nutritional interventions were feasible and safe for patients undergoing chemotherapy and demonstrated preliminary efficacy to improve nutritional status, fatigue, chemotherapy tolerance, and other outcomes.
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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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".