Patients' Perspectives and Experiences of Cannabinoids to Manage Symptoms of Chronic Kidney Disease: An In‐Depth Interview Study
Bibliographic record
Abstract
INTRODUCTION: People with kidney failure experience severe, often intractable symptoms, which affect quality of life. These symptoms are often unacknowledged and difficult to treat due to few treatment options being available. The use of cannabinoids to treat symptoms of long-term medical conditions is increasing worldwide. However, there is a paucity of data on the experiences of people using cannabis to treat the specific symptoms of kidney disease. METHODS: We conducted a qualitative semistructured interview study involving patients who had used either prescribed or nonprescribed cannabis to manage symptoms of kidney failure. Data were coded inductively to identify themes and a conceptual framework. RESULTS: Thirteen participants had used nonprescribed cannabis and four prescribed cannabis. Most participants had experience of treatment with hemodialysis. We identified four themes and subthemes: Managing overwhelming symptom burden (using cannabinoids to relieve symptoms, alleviating the mental load, restricted by a lack of options, feeling abandoned managing symptom burden); Barriers to accessing medical cannabis (constrained by finances and avoiding stigma and judgment); Weighing up risks of nonprescribed cannabis (wanting to avoid illegal behavior, avoiding potential harm, concerns of safety with treatment, worried about the safety of nonregulated products, seeking only medicinal benefits); Lack of clinician support (poor provision of information, lack of knowledge of prescribed options, valuing honesty). CONCLUSION: People with kidney failure describe frustration at the limited options for symptom management and appreciate the benefits of cannabis. Access to prescribed cannabis is restricted by legal issues and lack of clinician familiarity and confidence, which creates uncertainties and safety issues for patients when managing their symptoms.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".