Routes of administration, reasons for use, and approved indications of medical cannabis in oncology: a scoping review
Bibliographic record
Abstract
Abstract Introduction Some patients diagnosed with cancer use medical cannabis to self-manage undesirable symptoms, including nausea and pain. To improve patient safety and oncological care quality, the routes of administration for use of medical cannabis, patients’ reasons, and prescribed indications must be better understood. Methods Based on the Joanna Briggs Institute guidelines, a scoping review was conducted to map the current evidence regarding the use of medical cannabis in oncological settings based on the experiences of patients diagnosed with cancer and their healthcare providers. A search strategy was developed with a scientific librarian which included five databases (CINAHL, Web of Science, Medline, Embase, and PsycINFO) and two grey literature sources (Google Scholar and ProQuest). The inclusion criteria were: 1) population: adults aged 18 and over diagnosed with cancer; 2) phenomena of interest: reasons for cannabis use and/or the prescribed indications for medical cannabis; 3) context: oncological setting. French- or English-language primary empirical studies, knowledge syntheses, and grey literature published between 2000 and 2021 were included. Data were extracted by two independent reviewers and subjected to a thematic analysis. A narrative description approach was used to synthesize and present the findings. Results We identified 5,283 publications, of which 163 met the eligibility criteria. Two main reasons for medical cannabis use emerged from the thematic analysis: limiting the impacts of cancer and its side effects; and staying connected to others. Our results also indicated that medical cannabis is mostly used for three approved indications: to manage refractory nausea and vomiting, to complement pain management, and to improve appetite and food intake. We highlighted 11 routes of administration for medical cannabis, with oils and oral solutions the most frequently reported. Conclusion Future studies should consider the multiple routes of administration for medical cannabis, such as inhalation and edibles. Our review highlights that learning opportunities would support the development of healthcare providers’ knowledge and skills in assessing the needs and preferences of patients diagnosed with cancer who use medical cannabis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".