Age-related differences in cannabis use by cancer patients referred for supportive care.
Bibliographic record
Abstract
104 Background: Increasingly, cancer patients are using cannabis in their efforts to manage symptoms. Studies of legal recreational cannabis suggest young adults (YA) may disproportionately account for increases in cannabis use. There are few studies of cannabis use in cancer patients and its effect on symptoms, however, and most have not stratified their results by age. To address this, we examined rates of cannabis use across three age groups: YA ages 18 to 39, adults 40 to 65, and adults older than 65 (OA). We also examined the effects of age and cannabis use on patients’ report of cancer-related symptoms. Methods: We conducted a retrospective review of 1223 cancer patients referred to outpatient supportive care for symptom management between 2014 and 2017. Patients underwent urine drug testing for tetrahydrocannabinol (THC) and completed the Edmonton Symptom Assessment Scale-Revised-CSS during their initial visit. Results: In Chi square analysis, age was significantly associated with cannabis use (p < .001): 30% of YA tested positive for THC compared to 21% of adults and 8% of OA. As a group, cannabis users reported significantly higher scores for pain, tiredness, nausea, lack of appetite, anxiety, depression, difficulty sleeping, and worse overall well-being (p values < .05). In MANOVA, there was a significant interaction effect between age and cannabis use for pain, lack of appetite, shortness of breath, anxiety, depression, difficulty sleeping, and overall well-being (p values < .05). While YA and adults who tested positive for THC reported higher symptom scores for each of these symptoms, OA patients who were THC positive reported lower scores for pain, anxiety, depression, and better overall well-being. Conclusions: Findings suggest that compared to adults and OA, more YA patients are using cannabis in attempts to control cancer-related symptoms. With the exception of OA, cannabis users rate their cancer-related symptoms as more severe than nonusers. Findings support the need for patient education about potential therapeutic benefits and adverse effects of cannabis use in cancer. Prospective, observational studies are needed to characterize patients’ use before and after a cancer diagnosis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".