Effects of Prehabilitation With Advanced Technologies in Patients With Musculoskeletal Diseases Waiting for Surgery: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Prehabilitation delivered with advanced technologies represents a great chance for patients to optimize pre- and postoperative outcomes, reduce costs, and overcome travel-related barriers. Objective We aim to evaluate the effects of prehabilitation delivered with advanced technologies on clinically relevant outcomes among patients affected by musculoskeletal diseases and waiting for surgery. Methods We searched the PubMed, EMBASE, Cochrane Library, PEDro, and CINAHL databases up to February 2, 2023. ClinicalTrials.gov was also searched for registered protocols. Randomized controlled trials and nonrandomized intervention studies with adult participants of both sexes, affected by any musculoskeletal disease, and undergoing prehabilitation with advanced technologies or standard care were included. Study selection, data extraction, and critical appraisal were conducted in duplicate. Data were pooled for meta-analysis using random-effects models. Certainty of evidence was assessed for the primary outcome with the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system. The primary outcome was function. Secondary outcomes were pain, strength, risk of fall, autonomy in the activities of daily living, patient satisfaction, health-related quality of life, adverse events, and adherence to treatment. Results Six studies (7 reports), focusing on patients undergoing total knee or hip arthroplasty and primary meniscal tear and spine surgery were included. We found different prehabilitation programs: mindfulness-based stress reduction, exercise, education, or a combination thereof. Prehabilitation delivered with advanced technologies proved to be more effective in improving function in candidates for knee or hip replacement (Western Ontario McMaster Osteoarthritis Index “function” subscale before surgery: mean difference [MD] –7.45, 95% CI –10.71 to –4.19; I2=0%; after surgery: MD –7.84, 95% CI –11.80 to –3.88; I2=75.3%), preoperative pain (MD –1.67, 95% CI –2.50 to –0.48; I2=0%), risk of fall (MD –2.54, 95% CI –3.62 to –1.46; I2=0%), and postoperative stiffness (MD –2.00, 95% CI –2.01 to –1.99; I2=87%). No differences were found in pain 1 month after surgery. Data from studies including participants undergoing primary meniscal tear and spinal surgery could not be pooled. Conclusions Prehabilitation delivered with advanced technologies may be better than standard care in improving pre- and postoperative function among candidates for knee or hip arthroplasty. No quantitative results have been achieved on spine surgery candidates or other musculoskeletal diseases. Trial Registration PROSPERO CRD42022345811; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=345811
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 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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".