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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".