Risk Factors for Prolonged Opioid Use Following Total Hip Arthroplasty and Total Knee Arthroplasty: A Narrative Review of Recent Literature
Bibliographic record
Abstract
OBJECTIVE: To provide pharmacists and other health care professionals with the knowledge required to minimize the risk of prolonged opioid use following total hip arthroplasty (THA) and total knee arthroplasty (TKA). DATA SOURCES: A literature search of PubMed and Embase was performed, and included the search terms: (opioid OR opiate OR opium) AND (risk factor OR predict*) AND (arthroplasty OR replacement) NOT shoulder. STUDY SELECTION AND DATA EXTRACTION: Randomized control trials, cohort studies (both prospective and retrospective), systematic reviews, and meta-analyses were included if risk ratios (RRs) or odds ratios (ORs) were reported and published within the last 5 years. DATA SYNTHESIS: ]Twenty studies met inclusion criteria, including 2 meta-analyses and 2 prospective studies. There were several risk factors that overlapped between studies and presented clinically significant risks for prolonged opioid use following THA and TKA surgery. Of these, age < 65 (RRs: 1.15-9.36), preoperative opioid use (RRs: 1.09-7.81), larger quantities of opioids prescribed at discharge (RRs: 1.26-8.81), and TKA surgery (RRs: 1.73-6.07) were the most significant. Several risk factors were recently described, including migraines (RRs: 1.14-5.11) and fibromyalgia (RRs: 1.1-2.3) that may be of interest for further research. RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE: This review presents a discussion of the factors associated with prolonged opioid use following THA and TKA surgeries, which are among the most common orthopedic surgeries. CONCLUSIONS: Prescribers should carefully consider patient-specific factors when prescribing opioids as there are several factors, including age, surgery type, and medical conditions that can predispose patients to prolonged opioid use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".