Prehabilitation for Patients Undergoing Orthopedic Surgery
Bibliographic record
Abstract
Importance: Prehabilitation programs for patients undergoing orthopedic surgery have been gaining popularity in recent years. However, the current literature has produced varying results. Objective: To evaluate whether prehabilitation is associated with improved preoperative and postoperative outcomes compared with usual care for patients undergoing orthopedic surgery. Data Sources: Bibliographic databases (MEDLINE, CINAHL [Cumulative Index to Nursing and Allied Health Literature], AMED [Allied and Complementary Medicine], Embase, PEDRO [Physiotherapy Evidence Database], and Cochrane Central Register of Controlled Trials) were searched for published trials, and the Institute for Scientific Information Web of Science, System for Information on Grey Literature in Europe, and European clinical trials registry were searched for unpublished trials from January 1, 2000, to June 30, 2022. Study Selection: Randomized clinical trials (RCTs) comparing prehabilitation with standard care for any orthopedic surgical procedure were included. Data Extraction and Synthesis: Two independent reviewers screened trials. Data were pooled using a random-effects model. Recommendations were determined using the Grading of Recommendations Assessment, Development and Evaluation system and the study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Main Outcomes and Measures: Pain, function, muscle strength, and health-related quality of life (HRQOL). Results: Forty-eight unique trials involving 3570 unique participants (2196 women [61.5%]; mean [SD] age, 64.1 [9.1] years) were analyzed. Preoperatively, moderate-certainty evidence favoring prehabilitation was reported for patients undergoing total knee replacement (TKR) for function (standardized mean difference [SMD], -0.70 [95% CI, -1.08 to -0.32]) and muscle strength and flexion (SMD, 1.00 [95% CI, 0.23-1.77]) and for patients undergoing total hip replacement (THR) for HRQOL on the 36-item Short Form Health Survey (weighted mean difference [WMD], 7.35 [95% CI, 3.15-11.54]) and muscle strength and abduction (SMD, 1.03 [95% CI, 0.03-2.02]). High-certainty evidence was reported for patients undergoing lumbar surgery for back pain (WMD, -8.20 [95% CI, -8.85 to -7.55]) and moderate-certainty evidence for HRQOL (SMD, 0.46 [95% CI, 0.13-0.78]). Postoperatively, moderate-certainty evidence favoring prehabilitation was reported for function at 6 weeks in patients undergoing TKR (SMD, -0.51 [95% CI, -0.85 to -0.17]) and at 6 months in those undergoing lumbar surgery (SMD, -2.35 [95% CI, -3.92 to -0.79]). Other differences in outcomes favoring prehabilitation were of low to very low quality of evidence. Conclusions and Relevance: In this systematic review and meta-analysis of RCTs, moderate-certainty evidence supported prehabilitation over usual care in improving preoperative function and strength in TKR and HRQOL and muscle strength in THR, high-certainty evidence in reducing back pain, and moderate-certainty evidence in improving HRQOL in lumbar surgery. Postoperatively, moderate-certainty evidence supported prehabilitation for function following TKR at 6 weeks and lumbar surgery at 6 months. Prehabilitation showed promising results for other outcomes, although high risk of bias and heterogeneity affected overall quality of evidence. Additional RCTs with a low risk of bias investigating preoperative and postoperative outcomes for all orthopedic surgical procedures are required.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".