Cannabinoid Therapy
Bibliographic record
Abstract
OBJECTIVE: Clinical trials of cannabinoids for chronic pain have mixed and often inconclusive results. In contrast, many prospective observational studies show the analgesic effects of cannabinoids. This survey study aimed to examine the experiences/attitudes of individuals with chronic pain who are currently taking, have previously taken, or never taken cannabinoids for chronic pain to inform future research. METHODS: This study is based on a cross-sectional, web-based survey of individuals with self-reported chronic pain. Participants were invited to participate through an email that was distributed to the listservs of patient advocacy groups and foundations that engage individuals with chronic pain. RESULTS: Of the 969 respondents, 444 (46%) respondents reported currently taking, 213 (22%) previously taken, and 312 (32%) never taken cannabinoids for pain. Participants reported using cannabinoids to treat a wide variety of chronic pain conditions. Those currently taking cannabinoids (vs previously) more frequently reported: (1) large improvements from cannabinoids in all pain types, including particularly difficult-to-treat chronic overlapping pain conditions (eg, pelvic pain), (2) improvements in comorbid symptoms (eg, sleep), and (3) lower interference from side effects. Those currently taking cannabinoids reported more frequent and satisfactory communication with clinicians regarding cannabinoid use. Those never taken cannabinoids reported a lack of suggestion/approval of a clinician (40%), illegality (25%), and lack of FDA regulation (19%) as reasons for never trying cannabinoids. CONCLUSION: These findings underscore the importance of conducting high-quality clinical trials that include diverse pain populations and clinically relevant outcomes that if successful, could support FDA approval of cannabinoid products. Clinicians could then prescribe and monitor these treatments similarly to other chronic pain medications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.112 | 0.037 |
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".