Demographic and radiographic factors for knee symptoms and range of motion in patients with knee osteoarthritis: a cross-sectional study in Beijing, China
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) causes not only pain, stiffness, and dysfunction of the knee, but also the reduction of the joint range of motion (ROM). This study explored the demographic and radiographic factors for knee symptoms and ROM in patients with symptomatic KOA. METHODS: The demographic variables, Kellgren-Lawrence (KL) grade, and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) of patients with symptomatic KOA recruited in Beijing were collected. The knee ROM of all patients were also measured. We analyzed the influencing factors for WOMAC and ROM using a generalize linear model, respectively. RESULTS: This study included a total of 2034 patients with symptomatic KOA, including 530 males (26.1%) and 1504 females (73.0%), with a mean age of 59.17 (± 10.22) years. Patients with advanced age, overweight or obesity, a family history of KOA, a moderate-to-heavy manual labor job and use of nonsteroidal anti-inflammatory drugs (NSAIDs) had significantly higher WOMAC and lower ROM (all P < 0.05). The more the comorbidities, the higher the WOMAC (all P < 0.05). Patients with higher education had better ROM than those with only an elementary education(β = 4.905, P < 0.05). Compared with those KL = 0/1, the WOMAC of patients whose KL = 4 were higher (β = 0.069, P < 0.05), but the WOMAC of those KL = 2 were lower (β = -0.068, P < 0.05). ROM decreased with the increase of KL grade (all P < 0.05). CONCLUSIONS: KOA patients with advanced age, overweight or obesity, a family history of KOA in first-degree relatives, a moderate-to-heavy manual labor job tended to have more severe clinical symptoms and worse ROM. Patients with more severe imaging lesions tend to have poorer ROM. Symptom management measures and regular ROM screening should be taken early to these people.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".