Patient views regarding cannabis use in chronic kidney disease and kidney failure: a survey study
Bibliographic record
Abstract
BACKGROUND: Cannabis is frequently used recreationally and medicinally, including for symptom management in patients with kidney disease. METHODS: We elicited the views of Canadian adults with kidney disease regarding their cannabis use. Participants were asked whether they would try cannabis for anxiety, depression, restless legs, itchiness, fatigue, chronic pain, decreased appetite, nausea/vomiting, sleep, cramps and other symptoms. The degree to which respondents considered cannabis for each symptom was assessed with a modified Likert scale ranging from 1 to 5 (1, definitely would not; 5, definitely would). Multilevel multivariable linear regression was used to identify respondent characteristics associated with considering cannabis for symptom control. RESULTS: Of 320 respondents, 290 (90.6%) were from in-person recruitment (27.3% response rate) and 30 (9.4%) responses were from online recruitment. A total of 160/320 respondents (50.2%) had previously used cannabis, including smoking [140 (87.5%)], oils [69 (43.1%)] and edibles [92 (57.5%)]. The most common reasons for previous cannabis use were recreation [84/160 (52.5%)], pain alleviation [63/160 (39.4%)] and sleep enhancement [56/160 (35.0%)]. Only 33.8% of previous cannabis users thought their physicians were aware of their cannabis use. More than 50% of respondents probably would or definitely would try cannabis for symptom control for all 10 symptoms. Characteristics independently associated with interest in trying cannabis for symptom control included symptom type (pain, sleep, restless legs), online respondent {β = 0.7 [95% confidence interval (CI) 0.1-1.4]} and previous cannabis use [β = 1.2 (95% CI 0.9-1.5)]. CONCLUSIONS: Many patients with kidney disease use cannabis and there is interest in trying cannabis for symptom control.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".