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Record W4416500228 · doi:10.1302/1358-992x.2025.14.044

PROGNOSTIC FACTORS FOR INCREASED PAIN AND OPIOID CONSUMPTION FOLLOWING ARTHROSCOPIC KNEE AND SHOULDER SURGERY IN THE ACUTE POSTOPERATIVE PERIOD

2025· article· en· W4416500228 on OpenAlexaboutno aff
Hassaan Abdel Khalik, Ajaykumar Shanmugaraj, Seper Ekhtiari, Nolan S. Horner, Aaron Gazendam, Nicole Simunovic, Olufemi R. Ayeni

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsShoulder surgeryRotator cuffOpioidBody mass indexElbowLogistic regressionBicepsOsteoarthritisArthroscopy

Abstract

fetched live from OpenAlex

Various opioid-sparing strategies have demonstrated promising results following knee and shoulder arthroscopy. There is limited high-quality evidence exists shedding light on risk factors associated with increased post-operative opioid consumption and inferior pain outcomes following knee and shoulder arthroscopy, hence the purpose of this analysis. Using the dataset from the Non-Opioid Prescriptions after Arthroscopic Surgery in Canada (NO PAin) trial, eight prognostic factors were chosen a priori to evaluate their effect on opioid consumption and patient-reported pain following arthroscopic knee and shoulder surgery. These included age, sex, body mass index (BMI), tobacco use, alcohol use, number of comorbidities, employment status, and operative time. The primary analysis for this study was a multivariable linear regression using the number of OMEs consumed at 2- and 6-weeks postoperatively as the dependant variable. Secondary analyses included a multivariable linear regression with VAS pain scores at 2- and 6-weeks postoperatively as the dependant variable. Analyses of variance (ANOVAs) were conducted on categorical dependant variables found to be significant predictors of the model to assess whether statistically significant differences existed across these dependant variables’ categories. All tests were two-tailed with alpha = 0.05. A total of 193 patients were included in the final analysis. Included patients were primarily male (62.5%), with a mean age of 43.0 (SD, 15.3) years and a mean BMI of 29.2. Most patients underwent knee surgery (n=143; 73.1%). The most common procedures performed was a meniscectomy (n=93; 48.2%) in the knee and biceps tenotomy (n=25; 13.0%) in the shoulder. Tobacco usage was significantly associated with higher opioid usage at 2-weeks (p < 0 .001) and 6-weeks (p=0.02) postoperatively. Previous tobacco users had a higher 2-week (p=0.002) and cumulative OME (p=0.002) consumption compared to current and non-smokers. Age, sex, BMI, alcohol use, number of comorbidities, employment status and operative time were not significantly associated with number of OMEs consumed at 2- and 6-weeks postoperatively. Patients with a higher number of comorbidities (p=0.006) and those who were employed (p=0.006) reported higher pain scores at 6-weeks. Patients in the ‘not employed/other’ category had significantly lower pain scores at 6-weeks postoperatively (p=0.046). Age, sex, BMI, tobacco use, alcohol use, operative time, number of comorbidities and employment status were not significantly associated with worse VAS scores at either 2- and 6-weeks. Tobacco use status was significantly associated with increased post-operative opioid consumption following knee and shoulder arthroscopy at 2- and 6-weeks postoperatively, with former smokers consuming the largest quantity of opioids. Increased pain was found to be significantly associated with employment status and an increasing number of comorbidities at 6-weeks postoperatively. Particularly, employed patients presented with the highest VAS pain scores while unemployed patients presented with the lowest VAS pain scores. The findings of this analysis can further aid clinicians in identifying and mitigating increased opioid utilization as well as worse pain outcomes in high-risk patient populations undergoing arthroscopic surgery.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.283
Teacher spread0.268 · 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.

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 routes1
Has abstractyes

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