Patient-Reported Outcome Trajectories After Total Knee Arthroplasty in a Multiethnic Asian Cohort: A 5-Year Registry-Based Study
Bibliographic record
Abstract
Background Patient-reported outcomes (PROs) after total knee arthroplasty (TKA) are well-documented in Western populations, but long-term trajectories and their determinants in multiethnic Asian populations remain poorly understood. We aimed to determine the 5-year PRO trajectories after TKA and identify factors associated with long-term improvement in a multiethnic Asian cohort. Materials and Methods This registry-based cohort study used prospectively collected data from a tertiary hospital in Singapore. We included 4964 consecutive cases with osteoarthritis undergoing primary TKA between January 1, 2008, and December 31, 2023. The primary outcomes were changes in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total and subscale scores, measured at baseline and at 6 months, 1, 2, and 5 years postoperatively. Results The mean (SD) total WOMAC score improved from 38.6 (15.1) at baseline to 7.5 (9.2) at 5 years. The greatest improvement occurred within the first 6 months (mean change, 27.6 points; P < .001). In multivariable analysis, older age (≥ 75 years; OR 0.55, 95% CI 0.39–0.79) and the presence of one or more comorbidities (OR 0.83, 95% CI 0.71–0.96) were independently associated with lower long-term improvement. Recovery trajectories for pain and stiffness also differed significantly by ethnicity. Conclusion In this large, multi-ethnic Asian cohort, TKA provided substantial and durable improvements in PROs, primarily within the first 6 months. Long-term recovery trajectories, however, were independently associated with patient-level factors—notably age, comorbidity, and ethnicity—rather than surgical technique. These findings suggest that patient-centred risk stratification and culturally responsive perioperative care are critical for optimizing long-term outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".