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Record W4221029172 · doi:10.1186/s12891-022-05044-y

Strategies aimed at preventing long-term opioid use in trauma and orthopaedic surgery: a scoping review

2022· review· en· W4221029172 on OpenAlexaff
Caroline Côté, Mélanie Berube, Lynne Moore, François Lauzier, Louis A. Tremblay, Étienne L. Belzile, M-O Martel, M. Gabrielle Pagé, Y. Beaulieu, Anne-Marie Pinard, Kadija Perreault, Caroline Sirois, Sonia Grzelak, Alexis F. Turgeon

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2022
Typereview
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationCentre Hospitalier de l’Université de MontréalMcGill UniversityHealth Sciences CentreSunnybrook Health Science CentreUniversité de MontréalUniversité LavalCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleHôpital de l'Enfant-Jésus
Fundersnot available
KeywordsMedicineOrthopedic surgeryChecklistTrauma surgerySports medicineEvidence-based medicineMEDLINEMedical prescriptionChronic painPhysical therapyIntensive care medicineEmergency medicineAlternative medicineSurgeryPharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: Long-term opioid use, which may have significant individual and societal impacts, has been documented in up to 20% of patients after trauma or orthopaedic surgery. The objectives of this scoping review were to systematically map the research on strategies aiming to prevent chronic opioid use in these populations and to identify knowledge gaps in this area. METHODS: This scoping review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist. We searched seven databases and websites of relevant organizations. Selected studies and guidelines were published between January 2008 and September 2021. Preventive strategies were categorized as: system-based, pharmacological, educational, multimodal, and others. We summarized findings using measures of central tendency and frequency along with p-values. We also reported the level of evidence and the strength of recommendations presented in clinical guidelines. RESULTS: A total of 391 studies met the inclusion criteria after initial screening from which 66 studies and 20 guidelines were selected. Studies mainly focused on orthopaedic surgery (62,1%), trauma (30.3%) and spine surgery (7.6%). Among system-based strategies, hospital-based individualized opioid tapering protocols, and regulation initiatives limiting the prescription of opioids were associated with statistically significant decreases in morphine equivalent doses (MEDs) at 1 to 3 months following trauma and orthopaedic surgery. Among pharmacological strategies, only the use of non-steroidal anti-inflammatory drugs and beta blockers led to a significant reduction in MEDs up to 12 months after orthopaedic surgery. Most studies on educational strategies, multimodal strategies and psychological strategies were associated with significant reductions in MEDs beyond 1 month. The majority of recommendations from clinical practice guidelines were of low level of evidence. CONCLUSIONS: This scoping review advances knowledge on existing strategies to prevent long-term opioid use in trauma and orthopaedic surgery patients. We observed that system-based, educational, multimodal and psychological strategies are the most promising. Future research should focus on determining which strategies should be implemented particularly in trauma patients at high risk for long-term use, testing those that can promote a judicious prescription of opioids while preventing an illicit use, and evaluating their effects on relevant patient-reported and social outcomes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.356
Teacher spread0.298 · 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; both teacher heads agree on what is shown here.

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

Citations20
Published2022
Admission routes1
Has abstractyes

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