Effect of Comorbid Chronic Low Back Pain on Patient-Reported Outcome and Gait Parameters in Patients With Symptomatic Knee Osteoarthritis
Bibliographic record
Abstract
Knee osteoarthritis and chronic low back pain are common and often coexist. There are limited studies on the impact of coexisting musculoskeletal disorders on gait parameters and its association with self-assessed functional outcome. This study compared gait parameters, self-assessed functional outcome measurements, and quality-of-life scales between patients with knee osteoarthritis against those with coexisting knee osteoarthritis and chronic low back pain using gait analysis, Western Ontario and McMaster Osteoarthritis Index, and Short Form-36. Three hundred sixty-seven patients underwent gait analysis after the question-based functional outcome measurement. Pain, function, and quality of life were worse in the coexisting knee osteoarthritis and chronic low back pain group (n = 197) compared with the knee osteoarthritis only group (n = 170, P = 0.017, P = 0.004, P < 0.001, P = 0.004, respectively). The coexisting knee osteoarthritis and chronic low back pain group had significantly lower gait velocity and cadence than the knee osteoarthritis group (P = 0.028 and P = 0.003). The Western Ontario and McMaster Osteoarthritis Index Pain subscore was associated with gait velocity (P < 0.001) in the knee osteoarthritis group, whereas Short Form-36 physical composite was associated with gait velocity (P < 0.001) in the coexisting knee osteoarthritis and chronic low back pain group. Comorbid chronic low back pain in patients with knee osteoarthritis was associated with worse pain, function, quality of life, gait velocity, and cadence. Compared with the Western Ontario and McMaster Osteoarthritis Index, Short Form-36 may be a more suitable tool to track mobility outcome measure, such as gait velocity, in the management of the coexisting knee osteoarthritis and chronic low back 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".