Longitudinal Trajectories of Pain and Function Improvement Following Total Knee Replacement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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) | 0.001 | 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 it