Existence of a Neuropathic Pain Component in Patients with Osteoarthritis of the Knee
Bibliographic record
Abstract
PURPOSE: Pain from osteoarthritis (OA) is generally classified as nociceptive (inflammatory). Animal models of knee OA have shown that sensory nerve fibers innervating the knee are significantly damaged with destruction of subchondral bone junction, and induce neuropathic pain (NP). Our objective was to examine NP in the knees of OA patients using painDETECT (an NP questionnaire) and to evaluate the relationship between NP, pain intensity, and stage of OA. MATERIALS AND METHODS: Ninety-two knee OA patients were evaluated in this study. Pain scores using Visual Analogue Scales (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), painDETECT, duration of symptoms, severity of OA using the Kellgren-Lawrence (KL) system, and amount of joint fluid were evaluated and compared using a Spearman's correlation coefficient by rank test. RESULTS: Our study identified at least 5.4% of our knee OA patients as likely to have NP and 15.2% as possibly having NP. The painDETECT score was significantly correlated with the VAS and WOMAC pain severity. Compared with the painDETECT score, there was a tendency for positive correlation with the KL grade, and tendency for negative correlation with the existence and amount of joint fluid, but these correlations were not significant. CONCLUSION: PainDETECT scores classified 5.4% of pain from knee OA as NP. NP tended to be seen in patients with less joint fluid and increased KL grade, both of which corresponded to late stages of OA. It is important to consider the existence of NP in the treatment of knee OA pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".