The Therapeutic Potential and Usage Patterns of Cannabinoids in People with Spinal Cord Injuries: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: People with spinal cord injuries (SCI) commonly experience pain and spasticity; limitations of current treatments have generated interest in cannabis as a possible therapy. OBJECTIVES: We conducted this systematic review to: 1) examine usage patterns and reasons for cannabinoid use, and 2) determine the treatment efficacy and safety of cannabinoid use in people with SCI. METHODS: PubMed, Embase, Web of Science and Cumulative Index to Nursing and Allied Health Literature databases were queried for keywords related to SCI and cannabinoids. RESULTS: 7,232 studies were screened, and 34 were included in this systematic review. Though 26 studies addressed cannabinoid usage, only 8 investigated its therapeutic potential on outcomes such as pain and spasticity. The most common method of use was smoking. Relief of pain, spasticity and recreation were the most common reasons for use. A statistically significant reduction of pain and spasticity was observed with cannabinoid use in 83% and 100% of experimental studies, respectively. However, on examination of randomized control trials (RCTs) alone, effect sizes ranged from - 0.82 to 0.83 for pain and -0.95 to 0.09 for spasticity. Cannabinoid use was associated with fatigue and cognitive deficits. CONCLUSION: Current evidence suggests that cannabinoids may reduce pain and spasticity in people with SCI, but its effect magnitude and clinical significance are unclear. Existing information is lacking on optimal dosage, method of use, composition and concentration of compounds. Long-term, double-blind, RCTs, assessing a wider range of outcomes should be conducted to further understand the effects of cannabinoid use in people with SCI.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 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".