Knee Pain Predicts Subsequent Shoulder Pain and the Association Is Mediated by Leg Weakness: Longitudinal Observational Data from the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To assess whether the "spread" of joint pain is related to pain-associated muscle loss in 1 joint leading to increased loading and subsequent pain in other joints. METHODS: Associations between persistent knee pain (pain in 1 or 2 knees over 0-3 years vs no persistent pain) and incident shoulder pain at Year 4 were examined in participants from the longitudinal National Institutes of Health Osteoarthritis Initiative. Associations were assessed using log multinomial modeling, adjusted for age, sex, body mass index, depression score, other lower limb pain, and baseline leg weakness (difficulty standing from a sitting position). RESULTS: In older adults with clinically significant knee osteoarthritis (OA) or at risk of knee OA (n = 3486), the number of painful joints increased yearly, from 2.1 joints (95% CI 2.0-2.2) at baseline increasing by 5.2% (95% CI 2.2-8.3) at Year 4. Shoulders were the next most commonly affected joints after knees (28.5%). Persistent pain in 1 or 2 knees increased risk of bilateral shoulder pain at Year 4 [1 knee: relative risk (RR) 1.59, 95% CI 0.97-2.61; 2 knees: RR 2.02, 95% CI 1.17-3.49] after adjustment for confounders. Further adjustment for leg weakness attenuated effect sizes (1 knee: RR 1.13, 95% CI 0.60-2.11; 2 knees: RR 1.44, 95% CI 0.75-2.77), indicating mediation by functional leg weakness. CONCLUSION: Spread of joint pain is not random. Persistently painful knees predict new bilateral shoulder pain, which is likely mediated by leg weakness, suggesting that biomechanical factors influence the spread of 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".