Efficacy of cannabinoids for the prophylaxis of chemotherapy-induced nausea and vomiting—a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cannabinoids have potential efficacy as prophylaxis for chemotherapy-induced nausea and vomiting (CINV), but no recent meta-analysis has reported on their relative efficacy compared to other antiemetics. The aim of this meta-analysis is to examine the relative efficacy of cannabinoids for prophylaxis of CINV. METHODS: A literature search was conducted in OVID Medline, EMBASE, and Cochrane Central Register of Controlled Trials from inception up until March 2024. Articles were included if they reported on complete response, no nausea, no vomiting or no use of rescue medications, and were randomized controlled trials with cannabinoids in one arm. Meta-analysis was conducted for each endpoint and for a composite endpoint amalgamating existing endpoints. Subgroup analyses by medication used in control arm and by study design were conducted. Cumulative and leave-one-out analysis was also conducted. Type I error was set at 0.05. RESULTS: = 0.00%). However, there was no difference between cannabinoid and active treatment alternatives (most using dated single-agent regimens) for any outcomes. A recent phase II/III trial demonstrated superior efficacy of THC:CBD for secondary prevention of CINV when used as adjunctive therapy alongside modern antiemetic regimens, albeit mostly without olanzapine. CONCLUSIONS: There is scant evidence for efficacy of cannabinoids for CINV in the era of triple and quadruple antiemetics. Although THC:CBD showed promised in a recent trial, further trials should examine its safety and efficacy in the context of regimens containing olanzapine.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".