Factors Contributing to Variation in the Management of Radiation Therapy-Induced Nausea and Vomiting
Bibliographic record
Abstract
Purpose: Variation in the management of radiation therapy-induced nausea and vomiting (RINV) negatively impacts patients, practitioners and health systems and likely reflects limits in knowledge and failures to apply evidence. Factors that contribute to this variation are understudied. We investigated five factors that we believe contribute to variation in practice. Methods: 1) A web-based case-based international patterns of practice survey investigated RINV management strategies among radiation oncologists; 2) a systematic literature review investigated the timing and duration of prophylactic serotonin3 receptor antagonist (5-HT3RA) antiemetic therapy in randomized and non-randomized RINV trials; 3) another systematic review investigated the methodologies, endpoints and outcome measures of randomized RINV trials; 4) a prospective cohort study investigated RINV among patients receiving moderate- and low-risk palliative radiation therapy (RT) for bone metastases; and 5) another prospective cohort study investigated RINV among patients receiving long-course neoadjuvant pelvic RT and concurrent 5-fluorouracil-based chemotherapy for rectal adenocarcinoma. Results: Practice patterns varied among 1022 respondents from 12 countries, especially for moderate-risk and low-risk cases. The timing and duration of 5-HT3RA prophylaxis schedules and symptom control rates differed within 25 RINV trials. A diverse collection of methodologies, endpoints and outcome measures were observed in 34 RINV trials. RINV control rates were low within the bone metastases cohort (n=59 [32 evaluable]) with only 31% of patients being free of nausea and 44% being free of vomiting. Vomiting occurred in 21% of patients within the rectal cohort (n=34[33 evaluable]), however, the cumulative incidence of days with vomiting for all patients was only 1.6% of 1407 evaluable days. Conclusions: Our five complementary studies provide insight into factors likely contributing to variation in RINV management. These include a lack of knowledge about practice patterns in RINV management, an uncertainty regarding the ideal prophylactic administration of 5-HT3RAs, the heterogeneity of RINV clinical trial designs and outcome measures, and an incomplete understanding of the incidence of RINV among patients undergoing moderate-risk and low-risk palliative RT for bone metastases and long-course neoadjuvant pelvic RT for rectal adenocarcinoma.
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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 | 0.040 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".