Impact of an acute 1-month cannabidiol treatment on pain and inflammation after a long bone fracture: a triple-blind randomised, placebo-controlled, clinical trial protocol
Bibliographic record
Abstract
INTRODUCTION: Acute pain levels following orthopaedic injury (eg, fracture) are a predictor of the onset of chronic pain, which affects nearly 50% of fracture patients and impairs functional recovery. Among current pharmacological treatments for acute pain, non-steroidal anti-inflammatory drugs have been associated with delayed bone healing, while opioids inhibit effective bone remodelling, increase the risk of pseudarthrosis and carry a high risk of addiction. In light of this, the development of new pain treatments is essential. Cannabidiol (CBD), a non-addictive and non-psychotropic cannabis component stands out as a potential therapeutic agent, given its analgesic and anti-inflammatory properties as well as its potential benefits for bone healing. This randomised controlled trial aims to investigate the effect of acute CBD treatment, compared with placebo, on patients' self-reported pain, inflammation and well-being following a fracture injury. METHODS AND ANALYSIS: This is a triple-blind, randomised, placebo-controlled clinical trial. A total of 225 adults aged 18-70 years, who have suffered a long bone fracture and were treated at the Hôpital du Sacré-Coeur de Montréal, will be randomly assigned within 1 week to one of three treatment arms (25 mg or 50 mg of CBD or placebo) for 1 month. The primary outcome will be the difference in the pain score between groups at 1-month follow-up. Secondary outcomes will include measures of persistent pain, inflammation, opioid usage, quality of life, sleep quality, depression, anxiety, cognition and orthopaedic function. Data will be collected at baseline, 1-month and 3-month follow-ups. ETHICS AND DISSEMINATION: This study obtained a Health Canada licence for use of cannabis products. It has also been approved by Health Canada and the Research Ethics Board of the CIUSSS du Nord-de-l'Île-de-Montréal (Project ID 2025-2105). The findings will be published in a peer-reviewed journal and presented at local, national and international conferences. The trial's results will be made publicly available on the ClinicalTrials.gov database. TRIAL REGISTRATION NUMBER: NCT06448923.
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".