2023 Updated MASCC/ESMO Consensus Recommendations: prevention of radiotherapy- and chemoradiotherapy-induced nausea and vomiting
Bibliographic record
Abstract
PURPOSE: Radiotherapy and chemoradiotherapy-induced nausea and vomiting (RINV and C-RINV) are common and distressing, and there is a need for guidance for clinicians to provide up to date optimal antiemetic prophylaxis and treatment. Through a comprehensive review of the literature concerning RINV and C-RINV, this manuscript aims to update the evidence for antiemetic prophylaxis and rescue therapy and provide a new edition of recommendations for the MASCC/ESMO antiemetic guidelines for RINV and C-RINV. METHODS: A systematic review of the literature including data published from May 1, 2015, to January 31, 2023, was performed. All authors assessed the literature. RESULTS: The searches yielded 343 references; 37 met criteria for full article review, and 20 were ultimately retained. Only one randomized study in chemoradiation had the impact to provide new recommendations for the antiemetic guideline. Based on expert consensus, it was decided to change the recommendation for the "low emetic risk" category from "prophylaxis or rescue" to "rescue" only, while the drugs of choice remain unchanged. CONCLUSION: As for the previous guideline, the serotonin receptor antagonists are still the cornerstone in antiemetic prophylaxis of nausea and vomiting induced by high and moderate emetic risk radiotherapy. The guideline update provides new recommendation for the management of C-RINV for radiotherapy and concomitant weekly cisplatin. To avoid overtreatment, antiemetic prophylaxis is no longer recommended for the "low emetic risk" category.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".