Longitudinal Trajectories of Pain and Function Improvement Following Total Knee Replacement
Bibliographic record
Abstract
OBJECTIVE: Up to 30% of patients experience persistent pain and functional limitations following total knee replacement (TKR). Rapid symptom relief in the early postoperative period may be linked to longer-term outcome improvements. We sought to identify early improvement trajectories and to identify risk factors for suboptimal outcomes. METHODS: We used data from the Adding Value in Knee Arthroplasty (AViKA) Cohort study, a prospective longitudinal study of patients with knee osteoarthritis who underwent TKR. We assessed pain and function using the Western Ontario and McMaster Universities Arthritis Index (WOMAC). We used group-based trajectory modeling to identify distinct patterns of pain and function improvement over 6 months. We assessed the association between these early improvement trajectories and 24-month outcomes, including pain, function, and satisfaction. RESULTS: We analyzed data from 107 subjects. Mean baseline WOMAC pain and function scores were 42 (SD 17) and 44 (SD 15), respectively (0-100; 100 = worst). We identified two pain-improvement trajectories (suboptimal vs optimal improvement) and two function-improvement trajectories (suboptimal vs optimal improvement). Greater pain catastrophizing, worse mental health status, and use of a supportive device prior to TKR were associated with being in a suboptimal trajectory. Recipients of TKR in the suboptimal trajectories had higher pain, high functional disability, and lower satisfaction at 24 months post-TKR. CONCLUSION: Patients with slower improvement over the first 6 months post-TKR had worse outcomes at 24 months, suggesting that this early postoperative period may represent a window during which interventions aimed at speeding recovery may improve long-term TKR 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".