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Record W4395666441 · doi:10.1186/s13643-024-02528-x

Comparative benefits and harms of perioperative interventions to prevent chronic pain after orthopedic surgery: a systematic review and network meta-analysis of randomized trials

2024· review· en· W4395666441 on OpenAlexafffund
Mohammed Al-Asadi, Kian Torabiardakani, Andrea Darzi, Ian Gilron, Maura Marcucci, James S. Khan, Luis Enrique Chaparro, Brittany N. Rosenbloom, Rachel Couban, Andrew Thomas, Jason W. Busse, Behnam Sadeghirad

Bibliographic record

VenueSystematic Reviews · 2024
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsCanadian Armed ForcesWomen's College HospitalMcMaster UniversityGrand River HospitalQueen's UniversityImpactUniversity of Toronto
FundersChronic Pain Centre of Excellence for Canadian VeteransUniversity of Alberta
KeywordsMedicineRandomized controlled trialMeta-analysisPerioperativeOrthopedic surgeryPsychological interventionChronic painSystematic reviewPhysical therapyOrthopedic ProceduresMEDLINEAlternative medicineIntensive care medicineSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic postsurgical pain (CPSP) is common following musculoskeletal and orthopedic surgeries and is associated with impairment and reduced quality of life. Several interventions have been proposed to reduce CPSP; however, there remains uncertainty regarding which, if any, are most effective. We will perform a systematic review and network meta-analysis of randomised trials to assess the comparative benefits and harms of perioperative pharmacological and psychological interventions directed at preventing chronic pain after musculoskeletal and orthopedic surgeries. METHODS: We will search MEDLINE, Embase, PsycINFO, CINAHL, and the Cochrane Central Register of Controlled Trials from inception to present, without language restrictions. We will include randomised controlled trials that as follows: (1) enrolled adult patients undergoing musculoskeletal or orthopedic surgeries; (2) randomized them to any pharmacological or psychological interventions, or their combination directed at reducing CPSP, placebo, or usual care; and (3) assessed pain at 3 months or more after surgery. Screening for eligible trials, data extraction, and risk-of-bias assessment using revised Cochrane risk-of-bias tool (RoB 2.0) will be performed in duplicate and independently. Our main outcome of interest will be the proportion of surgical patients reporting any pain at ≥ 3 months after surgery. We will also collect data on other patient important outcomes, including pain severity, physical functioning, emotional functioning, dropout rate due to treatment-related adverse event, and overall dropout rate. We will perform a frequentist random-effects network meta-analysis to determine the relative treatment effects. When possible, the modifying effect of sex, surgery type and duration, anesthesia type, and veteran status on the effectiveness of interventions will be investigated using network meta-regression. We will use the GRADE approach to assess the certainty evidence and categorize interventions from most to least beneficial using GRADE minimally contextualised approach. DISCUSSION: This network meta-analysis will assess the comparative effectiveness of pharmacological and psychological interventions directed at preventing CPSP after orthopedic surgery. Our findings will inform clinical decision-making and identify promising interventions for future research. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023432503.

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)0.126
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMetaresearch, Meta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.265
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1260.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.1220.023
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.308
GPT teacher head0.446
Teacher spread0.138 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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