Clinical practice recommendations for prehabilitation and post‐operative rehabilitation for arthroplasty: A scoping review
Bibliographic record
Abstract
BACKGROUND: The rising need for arthroplasty (joint replacement) has resulted in a significant increase in wait-times. Longer surgical wait-times may further exacerbate functional decline in adults with osteoarthritis as well as delay postoperative functional recovery. This review aims to better inform rehabilitation care provision before (prehabilitation) and after (post-rehabilitation) hip or knee arthroplasty based on recommendations from clinical practice guidelines (CPGs). METHODS: This scoping review used a three-stage process to screen and extract articles, which resulted in 123 articles reviewed for analysis. Included CPGs were in the English language and focussed on rehabilitation interventions or practices involving adult patients preparing for or recuperating from hip and knee arthroplasty (published 2009-2020). RESULTS: Patient assessments, use of assistive devices, as well as self-management and education programs were recommended before and after arthroplasty. Physiotherapy was recommended to support post-operative rehabilitation. Conversely, there was limited evidence supporting recommendations for or against physiotherapy during the prehabilitation phase of the arthroplasty care journey. CONCLUSIONS: The findings from this review highlight the current gap in high-quality evidence supporting hip and knee arthroplasty rehabilitation CPGs before and after surgery. Findings warrant additional research to ensure patients are best prepared for surgery and supported for optimal recovery.
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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 | 0.031 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".