Predictors of Pain Reduction Among Fibromyalgia Patients Using Medical Cannabis: A <scp>Long‐Term</scp> Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Many patients with fibromyalgia (FM) report using cannabis as a strategy to improve pain. Given that pain often co-occurs with symptoms of anxiety and depression (i.e., negative affect) and sleep problems among patients with FM, improvements in these symptoms might indirectly contribute to reductions in pain intensity following cannabis use. The main objective of the study was to examine whether changes in pain intensity following initiation of medical cannabis among patients with FM could be attributed to concurrent changes (i.e., reductions) in negative affect and sleep problems. METHODS: This was a 12-month prospective cohort study among patients with FM (n = 323) initiating medical cannabis under the care of physicians. Patients were assessed at baseline, and follow-up assessment visits occurred every 3 months after initiation of medical cannabis. Patients' levels of pain intensity, negative affect, and sleep problems were assessed across all visits. RESULTS: Multilevel mediation analyses indicated that reductions in patients' levels of pain intensity were partly explained by concurrent reductions in sleep problems and negative affect (both P < 0.001). This remained significant even when accounting for patients' baseline characteristics or changes in medical cannabis directives over time (all P > 0.05). CONCLUSION: Our findings provide preliminary insight into the potential mechanisms of action underlying pain reductions among patients with FM who are using medical cannabis. Given the high attrition rate (i.e., 75%) observed in the present study at 12 months, our findings cannot be generalized to all patients with FM who are using medical cannabis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".