Risk factors for neuropathic pain in patients with knee osteoarthritis
Bibliographic record
Abstract
Objective To investigate the onset of neuropathic pain (NP) in patients with knee osteoarthritis (KOA) and related influencing factors. Methods A total of 313 KOA patients who attended the outpatient service of Department of Rehabi-litation Medicine in our hospital from July 2023 to October 2024 were enrolled, and according to the scoring results of the PainDETECT questionnaire, they were divided into NP group and non-NP group. The two groups were compared in terms of age, sex, body mass index (BMI), duration of knee pain symptoms, Kellgren-Lawrence (K-L) grading, pain Visual Analogue Scale (VAS) score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Hospital Anxiety and Depression Scale (HADS) score, and knee magnetic resonance imaging (MRI) findings. A logistic regression analysis was performed for the indicators with statistical significance identified by the univariate analysis to obtain the risk factors for NP. Results Of all patients, the patients with NP accounted for 16.61%. There were significant differences between the two groups in age, sex, pain VAS score, WOMAC, HADS score, and the proportion of patients with meniscus injury, soft tissue edema around the knee joint or knee joint ligament injury on MRI (H=16.272-49.953,χ2=12.897-23.792,P<0.05). The logistic regression analysis showed that old age, a high pain VAS score, a high HADS score, the presence of soft tissue edema around the knee joint, and the presence of knee joint ligament injury were risk factors for the onset of NP (P<0.05). Conclusion Old age, a high pain VAS score, a high HADS score, the presence of soft tissue edema around the knee joint, and the presence of knee joint ligament injury are risk factors for the onset of NP in KOA patients.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".