Relationship of neuropathic pain component and osteoarthritis stage in patients with knee osteoarthritis
Bibliographic record
Abstract
Aim: The aim of this study was to determine the frequency of neuropathic pain (NP) in patients with knee osteoarthritis (OA) and the relationship between knee OA stage and NP degree.Materials and Methods: This study included 63 patients (11 male, 52 female; mean age 62.4 ± 11.9 years; range 40-89 years) who were diagnosed with knee OA according to the American College of Rheumatology between January and May 2019. For all the participants, the knee pain was evaluated with the Visual Analog Scale (VAS), the functional evaluation was performed through Western Ontario and McMaster University (WOMAC) Osteoarthritis Index, and the neuropathic pain (NP) component was evaluated through Pain-DETECT Questionnaire (PDQ). The Kellgren-Lawrence grading system was used to determine radiological severity.Results: Of the total, 18 patients (28.5%) were classified as possible NP and 15 patients (23.5 %) were classified as likely NP. The symptom durations of the patients with final NP diagnosis were longer (p<0.05). The PDQ score was determined to be moderate and positively correlated with the WOMAC subscale scores and total score (pain score: r=0.450, p<0.001; stiffness score: r=0.4011, p=0.001; physical function score: r=0.397, p=0.001; total score: r=0.424, p=0.001). A positive and weak correlation was detected between the knee OA stage and neuropathic pain scores (p<0.05; r: 0.265).Conclusion: This study showed that the underlying cause of knee pain in the significant part of knee-OA patients has the NP component. Knee-OA patients with neuropathic pain component had higher rates of OA-related disability. There was a poor correlation between NP scores and radiographically advanced osteoarthritis symptoms. However, we suggest that further studies should be conducted with larger sample groups, the NP component should be kept in mind and should be included in the treatment plan for the knee-OA 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 | 0.000 |
| 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".