Efficacy of granisetron and aprepitant in a patient who failed ondansetron in the prophylaxis of radiation induced nausea and vomiting: a case report.
Bibliographic record
Abstract
BACKGROUND: Radiotherapy-induced nausea and vomiting (RINV) is a toxicity that can occur in 40-80% of individuals who receive radiation treatment. Current guidelines recommend 5-hydroxytryptamine3 receptor antagonists (5-HT3 RAs) for prophylaxis of RINV for moderate and highly emetogenic radiotherapy; however, certain patients may suffer from RINV despite prophylaxis. CASE PRESENTATION: This report details the case of a 47-year-old female with extensive bony involvement to the spine from breast cancer presenting with lower back pain. CASE MANAGEMENT: To palliate her symptoms, the patient underwent a course of irradiation to the lumbar spine and was prescribed ondansetron as an antiemetic. However, the patient experienced severe nausea and emesis and was subsequently switched to granisetron and aprepitant. CASE OUTCOME: The patient completed the remainder of the radiation treatment with no further emesis and minimal nausea, representing the first documented success of granisetron and aprepitant for RINV after failure on ondansetron. CONCLUSIONS: In chemotherapy, switching 5-HT3 RAs after failure on the first is successful in preventing chemotherapy-induced nausea and vomiting (CINV), yet this has not been previously reported in radiation. In this patient, granisetron and aprepitant were successful in substantially reducing nausea and preventing further emesis, and may represent an alternative antiemetic regimen for RINV prophylaxis and salvage.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".