Does Preoperative Rehabilitation Improve Patient‐Based Outcomes in Persons Who Have Undergone Total Knee Arthroplasty? A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Common clinical practice includes attempts to improve pain, function, motion, and patient independence before total knee arthroplasty (TKA) surgery. Although preoperative rehabilitation often is prescribed, it is unknown whether this care improves patient outcomes after TKA surgery. OBJECTIVE: To focus on how TKA preoperative rehabilitation affects quality of life, pain, and physical outcomes after surgery. DATA SOURCES: A computerized search was performed in February 2011. We searched PubMed, Ageline, CIHNAL, and SPORTDiscus from 1950 through February 2011 using combinations of the terms knee, rehabilitation, arthroplasty, preoperative, and presurgical. Searches were limited to "human" and "English" studies reported in peer-review journals. STUDY SELECTION: Seven studies met the inclusion criteria. For all variables, none was consistently favorable toward preoperative rehabilitation over alternative or control treatment. DATA EXTRACTION: Means and standard deviations (SDs) for each category of the Western Ontario and McMaster Osteoarthritis Index (WOMAC), range of motion, and length of stay (LOS) were obtained and served to calculate point measures and measures of variability. Specifically, bias-adjusted Hedges' g effect sizes, along with 95% confidence intervals, were calculated to assess the magnitude of the treatment effect for each outcome, with separate meta-analyses performed as a summary of the treatment response for each outcome. DATA SYNTHESIS: A total of 240 studies were identified in the search of the literature. On the basis of the title, abstract, or study content, 203 studies were excluded. Of the remaining 37 studies, 30 were excluded on the basis of study design and choice of outcome measures. The results of this review indicate that preoperative rehabilitation likely had no true treatment effect on WOMAC scores, range of motion, and LOS because all effect sizes were weak (<0.4) and confidence intervals crossed zero. CONCLUSION: For all outcomes, none was consistently favorable toward preoperative rehabilitation over the alternative for patients undergoing TKA with the exception of LOS in favor of the treatment group.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".