Effects of Aromatherapy on Chemotherapy-Induced Gastrointestinal Toxicity in Patients With Cancer: Protocol for a Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Chemotherapy-induced gastrointestinal toxicity (CIGT) is a common and distressing adverse effect in cancer care, manifesting as nausea, vomiting, appetite loss, oral mucositis, constipation, and diarrhea. These symptoms severely impair patients' quality of life, reduce treatment adherence, and may lead to premature therapy discontinuation. Aromatherapy, a complementary therapy using plant-derived essential oils, has shown potential benefits for alleviating CIGT symptoms; however, most existing systematic reviews focus solely on nausea and vomiting, leaving its effects on other CIGT symptoms under-studied. Moreover, the influence of essential oil types, intervention forms, and intervention durations on therapeutic outcomes remains unclear. Objective: This systematic review and meta-analysis aims to comprehensively evaluate the efficacy and safety of aromatherapy for the full spectrum of CIGT symptoms in patients with cancer and to clarify how essential oil types, intervention forms, and intervention durations influence treatment outcomes. Methods: Nine databases (PubMed, Cochrane Library, Web of Science, Embase, Cumulative Index to Nursing and Allied Health Literature, Chinese National Knowledge Infrastructure, Wanfang, Chinese Science and Technology Journal Database, and SinoMed); the World Health Organization (WHO) Trials Portal; and the Chinese Clinical Trial Registry will be searched from inception to August 2025 to identify randomized controlled trials focusing on aromatherapy for CIGT management in patients with cancer. Data on participant characteristics, interventions, comparisons, outcomes, and adverse effects will be extracted from included studies. Continuous outcomes will be synthesized using standardized mean differences with 95% CIs, and categorical outcomes will be summarized as odds ratios with 95% CIs. All analyses will adopt a random-effects model to account for expected clinical and methodological heterogeneity. Subgroup and meta-regression analyses will be conducted to examine differences across essential oil types, intervention forms, and intervention durations. The Hartung-Knapp-Sidik-Jonkman method will be used for random-effects estimation, and prediction intervals will be calculated where applicable to reflect real-world variation. Risk of bias will be assessed using the Cochrane Risk of Bias 2 tool, and evidence certainty will be graded using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. Results: This study was funded in February 2024. As of August 2025, the literature search and study selection have been completed, and 20 eligible randomized controlled trials have been identified. Data extraction and quantitative synthesis are expected to be completed in December 2025, and the final results are anticipated to be submitted for publication in March 2026. Conclusions: The anticipated findings will address key evidence gaps by evaluating aromatherapy's therapeutic potential for CIGT beyond nausea and vomiting and clarifying parameter-specific effects on CIGT management. These findings will support the development of evidence-based, standardized aromatherapy interventions, guide future mechanism-based research, and inform clinical decision-making in supportive cancer care.
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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 | 0.059 | 0.077 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.026 | 0.036 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.004 |
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".