Mechanical Pain is a Main Type of Pain in Patients With Advanced Knee Osteoarthritis
Bibliographic record
Abstract
Objectives This study aimed to investigate the prevalence and risk factors of mechanical pain in patients with advanced knee osteoarthritis (KOA), providing insights for targeted treatment approaches. Methods We conducted a cross‐sectional study involving 920 patients with KOA. The sample size was determined using the formula n = ( Z 2 ∗ P ∗(1 − P ))/ E 2 , assuming a 95% confidence interval (CI) and a 5% margin of error. Data on demographics and affected knee parameters, including age, sex, body mass index (BMI), affected side, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, range of motion, degree of varus, and numeric rating scale (NRS) were collected. Pain was categorized using the painDETECT questionnaire and WOMAC scores to differentiate between simple mechanical pain, mixed mechanical pain, and probable neuropathic pain (NP). Results Among participants, 43.48% experienced simple mechanical pain, 33.48% had mixed mechanical pain, and 23.04% reported probable NP. Significant differences were observed in the total WOMAC scores, range of motion (bend), and NRS across the three groups. Gender distribution varied significantly, with a higher proportion of female patients in each pain category. Notably, NRS on the affected side was moderately correlated with the total WOMAC pain score ( r = 0.500, ∗ p < 0.05). Moreover, female patients exhibited significantly higher WOMAC pain scores (6.28) compared with males (6.08), and women with a WOMAC pain score > 4 had an odds ratio (OR) of 2.462 (95% CI: 1.766–3.433, ∗ p < 0.05) compared with those with a score ≤ 4. Conclusions Mechanical pain is highly prevalent in patients with advanced KOA. Identifying the specific type of mechanical pain and associated risk factors, such as female gender and higher NRS score, can facilitate personalized pain management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".