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Record W4400321131 · doi:10.3390/healthcare12131335

Impact of Medical Cannabis on Recovery from Playing-Related Musculoskeletal Disorders in Musicians: An Observational Cohort Study

2024· article· en· W4400321131 on OpenAlexaffabout
Kathryn Cottrell, John P. Chong

Bibliographic record

VenueHealthcare · 2024
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsMcMaster Divinity College
Fundersnot available
KeywordsCannabisMedicineCannabidiolAnxietyAdverse effectPhysical therapyPopulationDepression (economics)Observational studyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Playing-related musculoskeletal disorders (PRMDs) are musculoskeletal symptoms that interfere with the ability to play at the level a musician is accustomed to. Musicians have an 84% lifetime prevalence of PRMD. Many types of analgesia are inappropriate for this population due to their risks, but cannabidiol (CBD) has been shown to have anti-inflammatory properties and can reduce the perception of pain. Medical cannabis has also been shown to be safer than other analgesia in terms of serious adverse events. This study explores the impact of medical cannabis for PRMD on perceptions of pain and mental health outcomes. Methods: Participants (n = 204) completed questionnaires at baseline and six months: the Musculoskeletal Pain Intensity and Interference Questionnaire for Musicians (MPIIQM) and Depression, Anxiety and Stress Scale (DASS-21). Participants self-selected their group: non-cannabis users (n = 42), new medical cannabis users (n = 61), and long-term medical cannabis users (n = 101). Data were analyzed using paired t-tests for within-group and ANOVA for between-group differences. Results: At six months, there was no difference (p = 0.579) in cannabidiol dose between new (24.87 ± 12.86 mg) and long-term users (21.48 ± 12.50 mg). There was a difference in tetrahydrocannabinol (THC) dose (p = 0.003) between new (3.74 ± 4.22 mg) and long-term users (4.41 ± 5.18 mg). At six months, new cannabis users had a significant reduction in pain intensity as measured by The Musculoskeletal Pain Intensity and Interference Questionnaire for Musicians (MPIIQM40) (p = 0.002). Non-users (p = 0.035), new users (p = 0.002), and long-term cannabis users (p = 0.009) all had significant reductions in pain interference (MPIIQM50) at six months. At six months, non-cannabis (p = 0.022) and long-term cannabis users (p = 0.001) had an improvement in DASS-21. The change in pain intensity was the only difference between groups, F(2, 201) = 3.845, p = 0.023. This difference was between long-term (0.83 ± 0.79) and new users (−2.61 ± 7.15). No serious adverse events occurred, and a minority experienced tiredness, cough, and dry mouth. Discussion/Conclusions: This practice-based evidence demonstrated that the multidimensional approach to care provided by the Musicians’ Clinics of Canada benefited all groups at six months. Medical cannabis significantly reduced pain intensity in new users of medical cannabis with PRMD, and all groups saw improvements in pain interference. In keeping with prior studies, medical cannabis seems to be effective at reducing perceptions of pain, including for PRMD. CBD/THC dosing was within guideline recommendations, and no patients experienced any serious adverse events. Limitations include multiple factors impacting patients’ decisions to opt in or out of medical cannabis. These include cost, comorbidities, and disease chronicity. In conclusion, medical cannabis reduces pain intensity in new users, and when combined with a multidimensional approach to care, patients with PRMD can see improvements in pain as well as mental wellbeing.

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)0.002
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.401
Teacher spread0.352 · 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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Citations1
Published2024
Admission routes2
Has abstractyes

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