Self-Reported Medical Cannabis Use and Inflammatory Cytokines and Chemokines in Chronic Pain Patients
Bibliographic record
Abstract
This dissertation investigated the association between medical cannabis (MC) use and inflammatory immune markers among chronic pain (CP) patients. Validated questionnaires, self-reported effectiveness of MC, and patient blood samples were collected. Fifty-six patients (64% females) were included with dried cannabis (53%) and THC-dominant products (70%) most commonly consumed. Majority of patients (83- 96%) self-reported symptom relief and and 76% reported a significant decrease in analgesic medication usage (p = <0.001). Compared to males, females had lower concentrations of some pro-inflammatory cytokines, cortisol, and significantly lower eotaxin levels (p=0.04). The regression analysis indicated that female sex was associated with decreased eotaxin (p = <0.01) concentrations. Blood CBD levels were associated with lower VEGF (p=0.04) concentrations and THC-COOH was a factor related to decreased TNF-α (p=0.02) and IL-12p70 (p=0.03). These findings suggest cannabis improves CP symptoms, reduces analgesic consumption, and has a potential immunomodulatory effect associated with patient sex and product type.
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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 | 0.000 | 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".