Determinants of analgesic responses following medical cannabis initiation among patients with chronic pain
Bibliographic record
Abstract
Background: Pain is one of the most common reasons for which patients visit healthcare providers. In Canada, chronic pain is a major health problem that affects about 20% of adults. Over the past two decades, there has been a rise in the use of cannabis for therapeutic purposes, and there is a growing body of research supporting the analgesic benefits of medical cannabis for patients with chronic pain. To date, however, little is known on the factors that contribute to the analgesic benefits of cannabis in these patients. Given that symptoms of anxiety and depression (i.e., negative affect) and sleep problems are linked to heightened clinical pain intensity, there is reason to believe that reductions in patients' negative affect and sleep problems resulting from cannabis use might indirectly contribute to reductions in pain intensity. It is possible that other patient-specific factors might also contribute to the analgesic benefits of medical cannabis, but these factors have remained largely unexplored. Objectives: The first objective of the present thesis was to examine if medical cannabis use was associated with reductions in “average” pain as well as with clinically significant (i.e., ≥ 30%) reductions in clinical pain intensity among patients with chronic noncancer pain. The second objective of the present thesis was to examine whether the association between medical cannabis use and reductions in clinical pain intensity was attributable to concurrent changes in negative affect and pain-related sleep interference. The third objective was to examine whether patient-specific characteristics were associated with reductions in clinical pain intensity following initiation of medical cannabis.Methods: In this longitudinal study, chronic noncancer pain patients (n = 2068) completed self-report measures assessing a host of sociodemographic, lifestyle, medical, and psychological variables. Measures assessing clinical pain intensity, negative affect, and pain-related sleep interference were completed at baseline and every three months, for a duration of one year, following initiation of medical cannabis. Results: Analyses first indicated that initiation of medical cannabis was associated with reductions in “average” pain intensity (p < .05). However, medical cannabis use was not significantly associated with clinically significant (i.e., ≥ 30%) reductions in pain intensity. Further analyses indicated that initiation of medical cannabis was associated with reductions in negative affect and pain-related sleep interference (both p's < .05). Interestingly, reductions in pain intensity following the initiation of medical cannabis remained significant even after controlling for concurrent reductions in negative affect and pain-related sleep interference (p < .05). Analyses subsequently showed that patient characteristics such as age and sex were significant predictors of reductions in average pain intensity following initiation of medical cannabis (both p's < .05). Conclusion: Findings from this thesis provide valuable new insights into our understanding of factors that may contribute to reductions in pain among patients with chronic pain who are using medical cannabis. Our results suggest that reductions in pain intensity following medical cannabis initiation are likely to be explained, in part, by concurrent reductions in negative affect and pain-related sleep interference. However, our findings indicated that reductions in pain following cannabis use cannot be entirely attributable to concurrent changes (i.e., reductions) in these variables. Finally, our findings indicated that the association between medical cannabis use and reductions in pain intensity was more pronounced among certain subgroups of patients, such as women and older patients. From a clinical point of view, our results could have implications for clinicians involved in the management of patients who might be considering medical cannabis as a therapeutic avenue
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".