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Record W3168645617 · doi:10.1101/2021.05.27.21257921

Long-term and serious harms of medical cannabis and cannabinoids for chronic pain: A systematic review of non-randomized studies

2021· review· en· W3168645617 on OpenAlexaff
Dena Zeraatkar, Matthew Cooper, Arnav Agarwal, Robin W.M. Vernooij, Gareth Leung, Kevin Loniewski, Jared Dookie, Muhammad Muneeb Ahmed, Brian Y. Hong, Chris J. Hong, Patrick Jiho Hong, Rachel Couban, Thomas Agoritsas, Jason W. Busse

Bibliographic record

VenuemedRxiv · 2021
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsWestern UniversitySeneca PolytechnicYork UniversityUniversity of TorontoMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineCannabisAdverse effectDiscontinuationChronic painSystematic reviewMEDLINERandomized controlled trialCochrane LibraryPsychiatryEffects of cannabisInternal medicine

Abstract

fetched live from OpenAlex

Abstract Objective To establish the risk and prevalence of long-term and serious harms of medical cannabis and cannabinoids for chronic pain. Design Systematic review and meta-analysis. Data sources MEDLINE, EMBASE, PsycInfo, and the Cochrane Central Register of Controlled Trials (CENTRAL) from inception to April 1, 2020. Study selection Non-randomized studies reporting on harms of medical cannabis or cannabinoids in people living with chronic pain with ≥4 weeks of follow-up. Data extraction and synthesis A parallel guideline panel provided input on the design and interpretation of the systematic review, including selection of adverse events for consideration. Two reviewers, working independently and in duplicate, screened the search results, extracted data, and assessed risk of bias. We used random-effects models for all meta-analyses and the GRADE approach to evaluate the certainty of evidence. Results We identified 39 eligible studies that enrolled 12,143 patients with chronic pain. Very low certainty evidence suggests that adverse events are common (prevalence: 26.0%; 95% CI 13.2 to 41.2) among users of medical cannabis or cannabinoids for chronic pain, particularly any psychiatric adverse events (prevalence: 13.5%; 95% CI 2.6 to 30.6). However, very low certainty evidence indicates serious adverse events, adverse events leading to discontinuation, cognitive adverse events, accidents and injuries, and dependence and withdrawal syndrome are uncommon and typically occur in fewer than one in 20 patients. We compared studies with <24 weeks and ≥ 24 weeks cannabis use and found more adverse events reported among studies with longer follow-up (test of interaction p < 0.01). Palmitoylethanolamide was usually associated with few to no adverse events. We found insufficient evidence addressing the harms of medical cannabis compared to other pain management options, such as opioids. Conclusions There is very low certainty evidence that adverse events are common among people living with chronic pain who use medical cannabis or cannabinoids, but that few patients experience serious adverse events. Future research should compare long-term and serious harms of medical cannabis with other management options for chronic pain, including opioids. Systematic review registration https://osf.io/25bxf What is already known on this topic Medical cannabis and cannabinoids are increasingly used for the management of chronic pain. Clinicians and patients considering medical cannabis or cannabinoids as a treatment option for chronic pain require evidence on benefits and harms, including long-term and serious adverse events to make informed decisions. What this study adds Very low certainty evidence suggests that adverse events are common among people living with chronic pain who use medical cannabis or cannabinoids, including psychiatric adverse events, though serious adverse events, adverse events leading to discontinuation, cognitive adverse events, accidents and injuries, and dependence and withdrawal syndrome are uncommon. There is insufficient evidence comparing the harms of medical cannabis or cannabinoids to other pain management options, such as opioids.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0140.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.399
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations14
Published2021
Admission routes1
Has abstractyes

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