The Association Between Low Back Pain and Osteoarthritis of the Hip and Knee: A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine whether prevalent self-reported back pain predicts future osteoarthritis-related pain and disability in individuals with hip and knee osteoarthritis (OA). METHODS: We studied a population-based cohort of Ontario residents who were 55 years or older and reported symptomatic hip/knee OA at baseline (between 1996 and 1998). The sample was followed-up between 2000 and 2001. We used multivariable linear regression to model the association between baseline back pain and pain and disability (Western Ontario and McMaster Universities Osteoarthritis Index scores) at follow-up while controlling for confounders. RESULTS: Of the 983 participants, 58% of the cohort reported low back pain at baseline. Baseline low back pain predicted higher hip and/or knee OA-related pain and disability at follow-up (beta = 2.49; 95% confidence interval [CI], 0.4-4.6; P = .023). However, this association varied with the location of OA. After controlling for confounders, the association was strong for individuals with hip OA (beta = 11.41; 95% CI, 5.1-17.7; P = .001). However, low back pain was not associated with pain and disability in individuals with knee OA (beta = 0; 95% CI -3.39 to 3.39; P = .998). CONCLUSIONS: In a cohort of individuals with OA of the hip or knee, we found that low back pain predicted subsequent OA-related pain and disability in those with hip disease, but not knee disease. Our study provides valuable prognostic information to clinicians involved in the management of patients with OA of the hip and knee.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".