Use of Pharmaceutical Analgesics Versus Cannabis or Cannabidiol-Tetrahydrocannabinol Oils to Reduce Pain
Bibliographic record
Abstract
Objective: Pain is a common symptom among opiate substitution patients.We surveyed those who recently attempted to control their pain with cannabidiol (CBD)-tetra hydrocanabinol (THC) oils or via cannabis.Materials and Methods: 18 patients in a methadone/suboxone clinic participated (age 29 to 56 years, mean=38.9,SD=7.8; 13 males, 5 females).Their mean number of years on pharmaceutical analgesics was 7.5 (SD=5.2,range 1 to 20).Their average severity of pain (rated on a scale from 0=no pain to 10=extreme pain) was 7.6 (SD=1.7,range 5 to 10).All 18 patients completed our questionnaire about their use of pharmaceutical analgesic medications, smoked or edible cannabis, CBD-THC oils, and the respective outcomes.Results: The average analgesic success rate (rated by the patients from 0=no relief to 10=pain eliminated) was 3.2 (SD=2.8)for pharmaceutical analgesics, 6.5 (SD=2.7)for smoked or edible cannabis, and 7.1 (SD=2.0)for CBD-THC oils.In our group of patients, the pharmaceutical analgesics reduced pain significantly less than CBD-THC oils (t=4.5, df=13, p<.001, 2-tailed) and also less than smoked or edible cannabis (t=3.3,df=13, p=.006, 2-tailed).The difference between smoked/edible cannabis and CBD-THC oils was not significant (p>.05).The majority of patients (62.5%) were able to stop their pharmaceutical analgesics when on CBD-THC oils.The more days on the oils, the longer lasted the relief (Spearman rho=.75, p=.013).Discussion: The duration of relief via cannabis might be more short-lived than from CBD-THC oils.Future studies need more control over the dose and composition of such oils.Conclusions: The CBD-THC oils are promising analgesics for further research and clinical work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".