Examining the roles of cannabinoids in pain and other therapeutic indications: a review
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: In recent times, our knowledge of cannabinoids and the endocannabinoid system has greatly advanced. With expanding knowledge, synthetic cannabinoids - including nabilone, dronabinol and a combination of synthetic Delta9-THC and cannabidiol - have been developed and tested for benefit in a variety of therapeutic indications. AREAS COVERED IN THIS REVIEW: The aim of this article is to provide a summative review of the vast amount of clinical trial data now available on these agents. WHAT THE READER WILL GAIN: To locate clinical trials for review, a literature search was performed using PubMed between the dates of 25 May and 30 June 2009. Search parameters were set to isolate only human randomized controlled trials (RCTs) published between 1990 and 2009. Keywords consistently used for each search include: cannabinoids, marijuana, THC, nabilone and dronabinol. Preferential selection was given to the best-designed trials, focusing on placebo-controlled, double-blind RCTs with the largest patient populations, if available. TAKE HOME MESSAGE: As efficacy and tolerability of these agents remain questionable, it is important that cannabinoids not be considered 'first-line' therapies for conditions for which there are more supported and better-tolerated agents. Instead, these agents could be considered in a situation of treatment failure with standard therapies or as adjunctive agents where appropriate.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".