Cannabinoids in the management of chronic pain: a front line clinical perspective
Bibliographic record
Abstract
Chronic pain is an escalating public health problem. Currently available treatments are inadequate to control chronic pain conditions, and there is a critical need for novel treatments. Over a half century of elegant preclinical research has identified the presence of a sophisticated endocannabinoid system that is part of our natural pain and immune defense network. Convergent work has supported the significant potential to exploit this system to decrease pain and inflammation. Although the clinical research remains in its infancy, recent systematic reviews have found that 25 of 30 randomized controlled trials have demonstrated a significant analgesic effect. The authors concluded that cannabinoids currently available for clinical use demonstrate a modest analgesic effect and are safe for the management of chronic pain. There is a critical need for more translational research so that the excellent work of Dr. Itai Bab and our basic science colleagues around the world can move forward in providing novel cannabinoid-based medicines. This should include more potent analgesics that are limited in side effects with several routes of delivery. Our patients deserve additional agents for pain control with a novel mechanism of action, and cannabinoids are the new frontier.
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".