Adherence to Consolidated Standards of Reporting Trials (CONSORT) Guidelines for Reporting Safety Outcomes in Trials of Medical Cannabis and Cannabis-based Medicines for Chronic Noncancer Pain
Bibliographic record
Abstract
OBJECTIVE: Current treatments for chronic pain have limited effectiveness and tolerability. With growing interest in the potential of cannabinoids, there is a need to inform risk-benefit considerations. Thus, this focused systematic review assesses the quality of safety assessment and reporting in chronic noncancer pain cannabinoid trials. METHODS: The protocol for this review has been published, and, registered in PROSPERO. We searched MEDLINE, Embase, The Cochrane Library, Scopus, and PsychINFO for double-blind, placebo-controlled, randomized controlled trials of cannabinoids for chronic pain, with a primary outcome related to pain. The primary review outcome is adherence to the 2004 Consolidated Standards of Reporting Trials (CONSORT) Harms extension. Secondary outcomes included type, reporting method, frequency and severity of adverse events (AEs), trial participant withdrawals, and reasons for withdrawals. RESULTS: In total, 43 studies (4436 participants) were included. Type of cannabinoid (number of studies) included nabiximols (12), dronabinol (8), nabilone (7), oral cannabis extract preparations (5), smoked tetrahydrocannabinol (5), vaporized tetrahydrocannabinol (3), novel synthetic cannabinoids (2), sublingual cannabis extract preparations (1). The median CONSORT score was 7. On average, 3 to 4 recommendations of the CONSORT guidelines were not being met in trials. Seventeen trials did not provide their method of AE assessment, 14 trials did not report on serious AEs and, 7 trials provided no quantitative data about AEs. DISCUSSION: Better harms assessment and reporting are needed in chronic pain cannabinoid trials. Improvements may be achieved through: expanded education/knowledge translation increased research regulation by ethics boards, funding agencies and journals, and greater emphasis on safety assessment and reporting throughout research training.
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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 | 0.571 | 0.665 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.025 | 0.037 |
| Bibliometrics | 0.024 | 0.032 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".