A randomized trial of nurse‐administered behavioral interventions to manage anticipatory nausea and vomiting in chemotherapy
Bibliographic record
Abstract
PURPOSE: Chemotherapy side effects diminish quality of life and can lead to treatment delay. Nausea and vomiting can occur prior to chemotherapy because of classical conditioning. We studied the effects of 20-minute behavioral interventions, administered by oncology nurses, of higher intensity (mindfulness relaxation-MR) or lower intensity (relaxing music-RM), on anticipatory nausea and vomiting (ANV). PATIENTS AND METHODS: Patients undergoing chemotherapy for solid tumors were randomized to MR (N = 160), RM (N = 159), or standard care SC (N = 155). Subjects were mostly female (91.8%) and white (86.1%) with breast cancer (85%). Most patients had early stage disease (Stage I: 26%; II: 52.9%; III: 19%; IV: 0.1%). Anticipatory nausea and vomiting were assessed at the midpoint and end of the chemotherapy course using the Morrow Assessment of Nausea and Emesis (MANE). RESULTS: Compared to SC, there was reduced anticipatory nausea at the midpoint of chemotherapy in those receiving MR (OR 0.44, 95% CI 0.20-0.93) and RM (OR 0.40, 95% CI 0.20-0.93), controlling for age, sex, cancer stage, and emetogenic level of chemotherapy. There was no difference between treatment groups in anticipatory nausea at the end of chemotherapy or in anticipatory vomiting and postchemotherapy nausea and vomiting at either time point. CONCLUSION: A brief nurse-delivered behavioral intervention can reduce midpoint ANV associated with chemotherapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".