MULTIDISCIPLINARY PERIOPERATIVE STRATEGIES TO IMPROVE OUTCOMES IN OSTEOARTHRITIS PATIENTS UNDERGOING JOINT REPLACEMENT: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Osteoarthritis (OA) is a leading cause of disability, and joint replacement remains the gold-standard intervention for advanced disease. Perioperative challenges such as pain, infection, and delayed mobilization necessitate comprehensive care strategies. Multidisciplinary perioperative interventions, including enhanced recovery after surgery (ERAS) protocols, prehabilitation, and preoperative optimization, have gained increasing attention. Objective: To synthesize evidence on the impact of multidisciplinary perioperative strategies on outcomes in OA patients undergoing hip or knee joint replacement. Methods: A systematic review was conducted following PRISMA guidelines. PubMed, Embase, MEDLINE, Scopus, and Cochrane Library were searched through March 2024. Eligible studies included adults undergoing arthroplasty for OA with perioperative multidisciplinary interventions. Outcomes assessed included pain, complications, length of stay (LOS), functional recovery, and patient satisfaction. Risk of bias was evaluated using Cochrane and Newcastle-Ottawa tools. Results: Eleven studies were included, comprising randomized controlled trials, cohort studies, systematic reviews, and narrative reviews. ERAS-based strategies consistently reduced LOS, opioid use, and complications, while improving functional outcomes and satisfaction. Subgroup analyses indicated particular benefits for elderly and high-risk patients, with nursing-led ERAS interventions enhancing recovery in frail populations. Prehabilitation showed mixed results, with potential benefits in patients with metabolic syndrome. Infection prevention optimization reduced per prosthetic joint infections. Study heterogeneity and variable quality limited comparability. Conclusion: Multidisciplinary perioperative strategies, especially ERAS protocols, improve clinical and patient-centered outcomes in OA patients undergoing joint replacement. Routine integration of such approaches is recommended, although further high-quality, standardized studies are needed to refine best practices.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".