Frequency of neuropathic pain and its impact on functional status in primary knee osteoarthritis patients
Bibliographic record
Abstract
Background: Neuropathic mechanisms are considered to play a role in development of pain in knee osteoarthritis (OA). Some OA patients developed sensitized central nociceptive circuits that enhance pain during various states of peripheral tissue insult. Method: 70 patients with primary knee OA were enrolled in this study. Antero-posterior knee radiographs were done using the Kellgren Lawrence scale. Pain severity was assessed by Numerical rating scale (NRS), neuropathic pain was assessed by Douleur Neuropathique en 4 (DN4) questionnaire and functional status was assessed by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale. Results: 52.9% of the patients had neuropathic pain according to DN4 scale. The mean pain WOMAC score and WOMAC physical function score were significantly higher in patients with neuropathic pain when compared to patients with nonneuropathic pain; 9.86 ± 2.1 versus 6.79 ± 3.59, P <0.0001 and 44.24 ± 5.43 versus 39.39 ± 10.36, P =0.015 respectively. DN4 score had a significant positive correlation with WOMAC pain (r=0.459,P<0.001), stiffness (r=0.258, P=0.031) and physical function (r=0.307, P=0.01). Conclusion: Chronic pain with OA has neuropathic components. Neuropathic pain is a factor that increases pain and disability and disrupts functional status in osteoarthritis 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".