Integrating nutrition, physical exercise, psychosocial support and antiemetic drugs into CINV management: The road to success
Bibliographic record
Abstract
Over the years, advancements in antiemetic drugs have improved chemotherapy-induced nausea and vomiting (CINV) control. However, despite the antiemetics therapies, in a relevant number of adult patients (∼30 %), CINV is still persistent, leading to several complications, such as electrolyte imbalances, anorexia, and treatment discontinuation. Supportive care interventions have gained credibility in cancer care, helping to improve patients' psycho-physical condition, quality of life, and managing symptoms, including CINV. Physical exercise and tailored nutritional counseling have demonstrated benefits in reducing the severity of nausea and vomiting. Psychological intervention has been postulated as a key approach in controlling anticipatory nausea/vomiting, as well as acupuncture/acupressure has been shown to decrease nausea and vomiting after chemotherapy treatments. In the current review, we aim to provide a clinical update on current prophylactic and delayed antiemetic guidelines for CINV and an overview of the non-pharmacological interventions tested for alleviating CINV in patients with cancer. • CINV still represents a crucial issue during chemotherapy/anticancer therapies in patients with cancer. • The pharmacological approach is the cornerstone of CINV treatment. • Other interventions, such as psychological, nutritional and physical, may contribute to the antiemetic efficacy. • A comprehensive approach combining drugs and supportive care could further optimize CINV management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".