Cannabinoid-based medicines in clinical care of chronic non-cancer pain: an analysis of pain mechanism and cannabinoid profile
Bibliographic record
Abstract
Aim: Among treatments for chronic non-cancer pain (CNCP), cannabinoid-based medicines (CBMs) have become extremely popular. Evidence remains modest and limited primarily to delta-9-tetrahydrocannabinol (THC) for neuropathic pain; nevertheless, the use of various CBMs, including cannabidiol (CBD) to treat neuropathic, nociceptive, and mixed pain has increased globally. This observational case-series assessed the impact of CBMs as a complementary treatment by pain mechanism and cannabinoid profile over three months. Methods: An analysis of patients with CNCP and treated with CBMs who consented to an ongoing registry was performed. Outcomes were patient-reported such as the Edmonton symptom assessment system-revised, brief pain inventory-short form, and 36-item short form health survey. Data from patients with complete outcomes for baseline and 3-month follow-up was extracted. Characteristics of adverse drug reactions (ADRs), including a description of the suspected product were also assessed. Results: A total of 495 patients were part of this analysis (mean age = 56 years old; 67% women). At 3-month, the proportional use of THC:CBD balanced and THC-dominant products increased. Patients with neuropathic pain had higher pain-severity scores vs. nociceptive pain. In addition to patients with neuropathic pain, patients with nociceptive and mixed pain also reported improvements in pain severity and secondary symptoms such as anxiety, depression, drowsiness, fatigue, sleep disturbances, and overall, health-related quality of life. THC-dominant treatment is more likely to be recommended when pain is severe, whereas CBD-dominant is favored for less severe cases. ADRs were more frequent among cannabis-naive patients and included dizziness, headache, and somnolence among others. Conclusions: Findings suggest that CBMs can be effective for neuropathic as well as nociceptive and mixed pain. THC is more frequently recommended for neuropathic and severe pain. Future research on CBMs in pain management must include details of CBM composition, and pain mechanism and must consider potential ADRs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".