Pilot Study of Patients Who Attempt to Stop Opiate Substitutes with Cannabidiol/Tetrahydrocanabinol
Bibliographic record
Abstract
Objective: Some patients seem able to reduce their dose of methadone by using the cannabidiol (CBD) or its mixtures with tetrahydrocannabinol (THC).We used a questionnaire to evaluate such cases. Materials and Method:We located 7 opiate substitution patients (6 males, one female) who were attempting to reduce their use of opiate substitutes via CBD/THC oils: one has been on suboxone for 5 years and 6 have been on methadone for 1.5 to 14 years.All 7 reported that they initially became addicted due to pain: their current pain severity (rated on a scale from 0=no pain to 10=extreme pain) ranged from 7 to 10 (mean=8.2,SD=1.2).They have been using CBD/THC oils for between 7 and 365 days.Results: Using the scale from 0 to 10 (0=no success; 10=CBD/THC oils helped to stop opiate substitutes completely), the patients' average was 5.7 (SD=3.2): the range was 0 to 8.Only one patient reported a complete failure to reduce the dose of his opiate substitute (suboxone) and of other concurrent analgesics.The pain reduction by these oils (rated from 0=no success to 10=pain eliminated) averaged at 6.4 (SD=3.4).The pain relief lasted, on average, for 18.5 hours (SD=14.0).Conclusions: While patients differ greatly in their biological characteristics and in their motivation to reduce the use of opiates or their substitutes, our data suggest that the CBD/THC oils might help many to achieve this goal, especially if provided with expert medical guidance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".