Cannabis use in palliative care: The prevalence and clinical characteristics.
Bibliographic record
Abstract
245 Background: Cannabis has growing attention in palliative care, been used for some cancer related symptom burden, but limited data in terms of prevalence in palliative care setting and clinical characteristics with using it. Purpose: To identify the prevalence of positive rate of cannabis metabolite on urine drug sample (UDS) and compare clinical characteristics focused on symptoms burden on Edmonton Symptom Assessment Scale (ESAS) on the same day of UDS. Methods: We conducted retrospective medical records review of 919 consecutive supportive care clinic patients who were seen at a National Cancer Institute center during a 12-month period between 7/01/2015 to 6/30/2016. Results: 531 out of 919 patients were excluded because UDS was not ordered: either patients were established or had low risk of substance abuse by clinicians’ judgement. 2 patients did not complete ESAS on same day of UDS. 137 patients were excluded because of missing UDS results as well. Finally, 249 out of 919 patients were included for data analysis with their UDS and ESAS at same day of visit. 54 patients were positive for cannabis metabolite (THC: tetrahydrocannabinol) on UDS (22%). We found that positive cannabis group was younger (Mean age 56.1 vs 48.8, p-Value .001), reported higher score of total ESAS (Mean 45.5 vs 38.9, p-value 0.023), pain (Mean 6.13 vs 4.99, p-Value 0.007), and insomnia (6.04 vs 4.44, p-Value 0.001). In addition, positive cannabis group reported poorer overall wellbeing (5.43 vs 4.56, p-Value 0.015) and spiritual wellbeing (6.04 vs 4.44, p Value 0.040) compared to negative cannabis group. Conclusions: The positive results of cannabis on UDS may be a marker of greater symptom burden, in particular, pain, insomnia and poorer overall and spiritual wellbeing as assessed by ESAS patient’s self-reporting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".