Early Postoperative Pain Predicts 2-Year Functional Outcomes following Knee Arthroplasty
Bibliographic record
Abstract
Abstract Pain control following knee arthroplasty is extremely important to both patients and surgeons to improve the perioperative experience; however, the implication of early pain control on long-term outcomes following knee arthroplasty remains poorly understood. We hypothesized that poor early pain control results in poor functional outcomes 2 years following total (TKA) and unicondylar knee arthroplasty (UKA). This retrospective study reviewed 242 TKA and 162 UKA performed at a single institution by two surgeons. Mean visual analog scale (VAS) pain scores were collected for first 3 postoperative days. Patients were prospectively evaluated using short form (SF-12), the Western Ontario and McMaster University osteoarthritis index (WOMAC), and the Knee Society functional score (KSFS) questionnaires. Pearson's correlation coefficients were calculated between mean VAS pain scores and functional outcome scores at 2 years. In the TKA group, poorly controlled perioperative pain correlated with poorer functional scores at 2 years. There was a significant negative correlation between early mean VAS pain scores (mean, 3.2 ± 2.0) and most 2-year functional outcomes including SF-12 physical score (r = −0.227, p ≤ 0.01), WOMAC pain scores (r = −0.268, p ≤ 0.01), WOMAC stiffness scores (r = −0.224, p < 0.01), WOMAC function score (r = −0.290, p 0.01), and KSFS (r = −0.175, p = 0.031). Better control of early pain was associated with improved functional outcomes at 2 years following TKA. We also found significant negative correlations between preoperative functional scores and early postoperative pain scores. Collectively, using preoperative and early postoperative pain scores, we identified an “at-risk” patient group that manifested an inferior functional outcome at 2 years; these patients may benefit from closer surveillance and a multidisciplinary approach to pain and function to optimize their clinical outcome following knee arthroplasty.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".