Post-surgical contributors to persistent knee pain following knee replacement: The Multicenter Osteoarthritis Study (MOST)
Bibliographic record
Abstract
Objective: Pain persistence following knee replacement (KR) occurs in ∼20-30% of patients. Although several studies have identified preoperative risk factors for persistent post-KR pain, few have focused on post-KR contributing factors. We sought to determine whether altered nociceptive signaling and other peripheral nociceptive drivers present post-operatively contribute to post-KR pain. Design: We included participants from the Multicenter Osteoarthritis Study who were evaluated ∼12 months after KR. We evaluated the relation of measures of pain sensitivity [pressure pain threshold (PPT), temporal summation (TS), and conditioned pain modulation (CPM)] and the number of painful body sites to post-KR WOMAC knee pain, and of the number of painful sites to altered nociceptive signaling using linear or logistic regression models, as appropriate. Results: 171 participants (mean age 69 years, 62% female) were included. TS was associated with worse WOMAC pain post-KR (β = 0.77 95% CI:0.19-1.35) and reduced odds of achieving patient acceptable symptom state (aOR = 0.54 95%CI:0.34-0.88). Inefficient CPM was also associated with worse WOMAC pain post-KR (β = 1.43 95% CI:0.15-2.71). In contrast, PPT was not associated with these outcomes. The number of painful body sites present post-KR was associated with TS (β = 0.05, 95% CI:0.01, 0.05). Conclusions: Post-KR presence of central sensitization and inefficient descending pain modulation was associated with post-KR pain. We also noted that presence of other painful body sites contributes to altered nociceptive signaling, and this may thus also contribute to the experience of knee pain post-KR. Our findings provide novel insights into central pain mechanisms and other peripheral pain sources contributing to post-KR persistent knee pain.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".