Prevention and management of chemotherapy-induced nausea and vomiting
Bibliographic record
Abstract
Nausea and vomiting are among the most frequently experienced toxic side-effects associated with chemotherapy. Although nausea and vomiting can result from surgery or radiotherapy, chemotherapy-induced nausea and vomiting (CINV) is potentially the most severe and most distressing. Estimates regarding the incidence of CINV vary depending on the treatment administered and individual patient characteristics.The impact of CINV on quality of life (QoL) and daily activities is considerable. Pharmacological treatments are considered routine for CINV. Clinical guidelines now recommend that patients receiving moderate emetic chemotherapy (MEC) regimens be preferentially treated with palonosetron, the 5-hydroxytryptamine type 3 (5-HT3) receptor antagonist, in combination with dexamethasone. In addition, it has shown that single-dose fosaprepitant is equivalent to the standard 3-day aprepitant regimen (the neurokinin 1 (NK1) receptor antagonist). Despite these advances in antiemetic management, approximately 50% of patients receiving chemotherapy still experience nausea and/or vomiting. Further improvements are still desirable, particularly in the prevention and treatment of delayed CINV. Non-pharmacological interventions can be possible adjuncts to standard anti-emetic therapy. Using new technologies to collect patient-reported outcomes may improve the accuracy of assessment, provide a better picture of the patient's experience of these symptoms, and provide a means to simultaneously monitor symptoms, educate patients, and collect longitudinal data.
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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) | 0.000 | 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 | 0.000 |
| 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".