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Record W4416207345 · doi:10.1302/1358-992x.2025.13.126

SELF-REPORTED RATES OF CANNABINOID USE IN PATIENTS WITH END-STAGE OSTEOARTHRITIS

2025· article· en· W4416207345 on OpenAlexaffabout
P. Abdelnour, Michael Tänzer, Adam Hart

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCannabinoidOsteoarthritisCannabisCannabinoid Receptor AgonistsMedical prescriptionOpioid

Abstract

fetched live from OpenAlex

Since 2018, cannabis usage rates in Quebec have risen from 14% to 19% following the legalization of cannabinoid use in Canada1. Although multifactorial, it seems the decrease in stigma around cannabinoid use following its recent legalization could explain the increase in usage rates2. Moreover, this decrease in stigma reduces a significant barrier for patients with osteoarthritis (OA) who considered using cannabinoids to manage their symptoms. Studies have shown that cannabinoid use in joint arthroplasty patients had positive analgesic effects and decreased the use of opioids3,4. However, other studies have found that cannabinoids are not effective in managing pain, are associated with negative outcomes, and are correlated with an increase in opioid use in arthroplasty patients5,6. Despite the conflicting evidence in the literature, cannabinoid use likely plays a significant role in patient care. However, it is difficult for healthcare workers to understand that role due to lack of data. The purpose of this study is to 1) document self-reported cannabinoid use among patients with knee and hip OA, and 2) investigate for clinico-demographic factors associated with cannabinoid use in knee and hip OA patients. One hundred and fifty-one patients were recruited. Due to missing data, 3 patients were excluded resulting in 148 participants (59 male, 89 female), including 94 knee OA patients and 54 hip OA patients. Data on age, sex, height, weight, socioeconomic status, tobacco use, alcohol use, and cannabinoid use were collected. The overall cannabinoid usage rate was 16%, with 15% using cannabinoids regularly. More specifically, regular cannabinoid use in patients having at least 7 drinks of alcohol a week was over 37%. In patients 55 years old and above, over 15% tried cannabinoids while 14% use them regularly. Younger age (mean difference of 5.8 years between groups; p=0.016), more pack years (mean difference of 8.4 pack years between groups; p=0.005), over 5 years of tobacco use (odds ratio [OR], 4.2 [95% confidence interval [CI], 1.6–10.9]; p=0.004), over 10 years of tobacco use (OR, 4.1 [95% CI, 1.5–10.8]; p=0.007), over 4 cigarettes consumed per day (OR, 3.1 [95% CI, 1.2–8.1]; p=0.024), and 7 or more alcoholic drinks per week (OR 1≥ : 7≤ dr/wk, 3.6 [95% CI, 1.2–12.2], OR 2-6 : 7≤ dr/wk, 4.8 [95% CI, 1.1–20.5]; p=0.045) were significantly associated with self-report cannabinoid use. Following the legalization of cannabinoids, the overall cannabinoid usage rate of over 16% in knee and hip OA patients resembles that of other US states with recently legalized cannabinoid use. Compared to the general population, older patients have significantly higher cannabinoid usage, suggesting that a large portion of these patients might be exploring cannabinoids as a means to manage their symptoms. These findings emphasize that a considerable proportion of end-stage OA patients use cannabinoids thereby creating the need for healthcare professionals to better understand the implications of its use, the patient outcomes, and the guidelines for cannabinoid use. Further studies are needed to explore the long-term effects of cannabinoid use in managing OA symptoms.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.263
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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