Is There a Possible Neuropathic Pain Component in Knee Osteoarthritis?
Bibliographic record
Abstract
OBJECTIVES: This study aims to investigate the neuropathic pain (NP) component in patients with osteoarthritis (OA) of the knee and its association with physical function, risk factors, and stages of OA. PATIENTS AND METHODS: One hundred and nine patients (16 males, 93 females; mean age 62.5±8.5 years; range 44 to 81 years) diagnosed with knee OA according to the American College of Rheumatology criteria were enrolled in this study between July 2014 and June 2015. Patients were evaluated with visual analog scale for pain severity, PainDETECT questionnaire for presence and severity of neuropathic pain, Western Ontario and McMaster Universities osteoarthritis index for physical function, and the Kellgren-Lawrence system for severity of OA. Presence of the associated risk factors were also questioned. RESULTS: A total of 12 patients (11%) were classified as having likely NP and 23 patients (21.1%) were classified as having possible NP. PainDETECT scores were significantly correlated with the visual analog scale scores and Western Ontario and McMaster Universities osteoarthritis index pain, physical function and total scores. Patients with neuropathic pain had significantly longer symptom duration than the patients without NP. However, we found no relationship between the other risk factors and NP. CONCLUSION: This study demonstrated that some of the knee OA patients had a NP component as the underlying cause of knee pain. Patients with NP had longer symptom duration, increased severity of pain, and disability. Therefore, the presence of NP component in these patients should be considered. Once it is determined, appropriate intervention strategies for NP should be incorporated in the routine treatment modalities of knee OA.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".