Comparing the effects of medical cannabis for chronic pain patients with and without co-morbid anxiety: A cohort study
Bibliographic record
Abstract
INTRODUCTION: There is growing evidence on the efficacy of cannabis-based medicinal products (CBMPs) for chronic pain (CP). Due to the interaction between CP and anxiety, and the potential impact of CBMPs on both anxiety and CP, this article aimed to compare the outcomes of CP patients with and without co-morbid anxiety following CBMP treatment. METHODS: Participants were prospectively enrolled and categorized by baseline General Anxiety Disorder-7(GAD-7) scores, into 'no anxiety'(GAD-7 < 5) and 'anxiety'(GAD-7 ≥ 5) cohorts. Primary outcomes were changes in Brief Pain Inventory Short-Form, Short-form McGill Pain Questionnaire-2, Pain Visual Analogue Scale, Sleep Quality Scale (SQS), GAD-7 and EQ-5D-5L index values at 1, 3 and 6 months. RESULTS: 1254 patients (anxiety = 711; no anxiety = 543) met inclusion criteria. Significant improvements in all primary outcomes were observed at all timepoints (p < 0.050), except GAD-7 in the no anxiety group(p > 0.050). The anxiety cohort reported greater improvements in EQ-5D-5L index values, SQS and GAD-7(p < 0.050), but there were no consistent differences in pain outcomes. CONCLUSION: A potential association between CBMPs and improvements in pain and health-related quality of life (HRQoL) in CP patients was identified. Those with co-morbid anxiety reported greater improvements in HRQoL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".