Nausea and vomiting induced by gastrointestinal radiation therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Radiation therapy-induced nausea and vomiting (RINV) are common and troublesome symptoms among patients receiving radiation therapy for gastrointestinal cancers. Their impact on function, quality of life and, ultimately, cancer control warrant a review of their incidence, underlying mechanisms, treatments and research themes. RECENT FINDINGS: Research in RINV is underrepresented relative to that in chemotherapy-induced nausea and vomiting. The incidence of RINV among patients receiving modern day radiation therapy is questioned and supportive care practice patterns vary among radiation oncologists. Antiemetic guideline recommendations for prophylactic and rescue therapy are based solely on the anatomic region being irradiated and not other patient-related, radiation therapy-related, or organ-specific dosimetric factors that likely modulate the risk of RINV. Dosimetric predictors are likely the most attainable biomarker moving forward, but only early steps have been taken. The small bowel and stomach will be the best first candidates for study among patients with gastrointestinal cancers. Studies of the mechanisms underlying RINV are conspicuously lacking. A new generation of observational studies and therapeutic clinical trials is needed, and more attention must be given to the relative impact of nausea and vomiting on the function and quality of life among specific homogeneous patient populations. SUMMARY: Optimal supportive care strategies for RINV following radiation therapy for gastrointestinal cancers are lacking, and will not be known until future research answers the many open questions regarding the mechanisms underlying RINV, the true incidence and impact of these symptoms among patients and the best way to predict and mitigate them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".