Musculoskeletal Symptomatic Areas After Total Knee Replacement for Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to measure cumulative incidence and incidence rate and identify factors associated with new musculoskeletal (MSK) symptomatic areas after total knee replacement (TKR) for osteoarthritis (OA). METHODS: Using data from a randomized controlled trial of patients undergoing elective TKR for OA, we assessed for MSK symptomatic areas by region (neck, hands/wrists/arms/shoulders, back, hips, nonindex knee, and ankles/feet) at baseline (pre-TKR), and at 3, 6, 12, 24, 36, and 48 months post-TKR. Cumulative incidence and incidence rates were calculated for each region. Factors associated with incident MSK symptomatic areas were identified using generalized linear mixed models. Time to incident symptomatic area was assessed using Cox proportional hazards regression. RESULTS: Among 293 subjects, the cumulative incidence of any new MSK symptomatic area over 4 years was 45%; the incidence rate was 19.2 per 100 person-years. Body site-specific cumulative incidence and incidence rates were highest for nonindex knee and back. Predictors of incident MSK symptomatic areas included female sex (relative risk [RR] 1.64; 95% confidence interval [CI] 1.15-2.34), body mass index of 35 or higher (RR 1.27; 95% CI 0.88-1.85), Charlson Comorbidity Index 2 or more (RR 1.28; 95% CI 0.92-1.78), baseline index knee Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score greater than 40 (RR 1.39; 95% CI 0.99-1.95), and anxiety/depression (measured by the five-item Mental Health Index) (RR 1.70; 95% CI 1.20-2.40). CONCLUSION: Incident MSK symptomatic areas occurred in roughly half of recipients of TKR in the 4 years after the operation. Further study is needed to examine the long-term impact of MSK symptomatic areas on postoperative pain, function, and quality of life.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".