Cannabinoid Regulation of Acute and Anticipatory Nausea
Bibliographic record
Abstract
Chemotherapy-induced nausea is one of the most distressing symptoms reported by patients undergoing treatment, and even with the introduction of newer antiemetics such as ondansetron and aprepitant, nausea remains problematic in the clinic. Indeed, when acute nausea is not properly managed, the cues of the clinic can become associated with this distressing symptom resulting in anticipatory nausea for which no effective treatments are available. Clinical trials exploring the potential of exogenous or endogenous cannabinoids to reduce chemotherapy-induced nausea are sparse; therefore, we must rely on the data from pre-clinical rat models of nausea. In this review, we explore the human and pre-clinical animal literature examining the potential for exogenous and endogenous cannabinoid treatments to regulate chemotherapy-induced nausea. The pre-clinical evidence points to a compelling need to evaluate the antinausea potential of cannabidiol, cannabidiolic acid, and treatments that boost the functioning of the endocannabinoid system in human clinical trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".