A review of the evidence surrounding the safety of medical marijuana authorization for adults with neuropathic pain in primary care
Bibliographic record
Abstract
Chronic neuropathic pain (NeP) is a complex condition that is commonly seen in primary care and is often refractory to current recommended treatments. Novel approaches to pain management are increasingly being studied to address this issue including the use of cannabis, a plant that has been used medicinally for thousands of years. The aim of this project was to review the current literature to determine if medical marijuana can be authorized safely by primary care providers (PCPs) to treat NeP in adults. Rational prescribing guidelines were used as the foundation for determining safety. Background knowledge of pain, chronic pain, neuropathic pain, analgesia, the history of medical marijuana, marijuana licensing, pharmacology of cannabis, including what is known about the efficacy of NeP, medical marijuana and society, nurse practitioner prescriptive authority and safe prescribing practices formed the basis of this review. The 12 studies utilized in this review do not provide enough data to support the safe use of medical marijuana for NeP in adults. It may be considered after guideline recommended prescription treatments have failed in specific clients taking into account the limitations of the evidence and associated risks. For those PCPs who are considering authorizing dried cannabis for their clients recommendations for practice will be discussed. Areas for future research and limitations of the review will also be acknowledged. --Leaf v.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".