Bibliographic record
Abstract
Nabilone, a synthetic cannabinoid, is approved in many countries including, but not limited to, Canada, the United States, Mexico, and the United Kingdom for the treatment of severe nausea and vomiting associated with chemotherapy. Clinical evidence is emerging for its use in managing pain conditions with different etiologies. We review the efficacy and safety of nabilone for various types of pain as well as its abuse potential, precautions and contraindications, and drug interactions; summarize pertinent clinical practice guidelines; and provide recommendations for dosing, monitoring, and patient education. Citations involving nabilone were identified through systematic reviews evaluating cannabinoids for pain. A systematic search (updated July 23, 2015) of the Ovid MEDLINE, EMBASE, PubMed, and Cochrane Library databases was performed. Eight randomized controlled trials, two prospective cohort trials, and one retrospective chart review were retrieved. Cancer pain, chronic noncancer pain, neuropathic pain, fibromyalgia, and pain associated with spasticity were the pain conditions evaluated. Nabilone was most commonly used as adjunctive therapy and led to small but significant reductions in pain. The most common adverse drug reactions included euphoria, drowsiness, and dizziness. Nabilone was rarely associated with severe adverse drug reactions requiring drug discontinuation, and the likelihood of abuse was thought to be low. Although the optimal role of nabilone in the management of pain is yet to be determined, certain clinical practice guidelines consider nabilone as a third-line agent.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".