Association Between Plasma Beta-endorphin and WOMAC Score in Female Patients with Knee Osteoarthritis
Bibliographic record
Abstract
BACKGROUND: β-endorphin plays a role in the descending pain control in the central nervous system. Central sensitization may be involved in the generating and maintenance of osteoarthritis (OA) pain. However, the correlation between β-endorphin and pain severity in OA has shown conflicting results. The aim of this study was to investigate the association between plasma β-endorphin and the severity of the disease. METHODS: This study was an observational cross-sectional study carried out on 60 female subjects with knee OA who fulfilled the inclusion criteria. Plasma β-endorphin was measured by a commercial enzyme-linked immunosorbent assay (ELISA) kit. Osteoarthritis knees were classified by the Kellegren-Lawrence (KL) grading (1-4) criteria. The Western Ontario McMaster University Osteoarthritis (WOMAC) scoring method was used to assess self-reported physical function, pain and stiffness. RESULTS: The mean of the participants' ages was 58 years old, ranging from 42 to 83 years. Overall, more than 70% of the participants were overweight with a mean of body mass index (BMI) of 27.59. More than 54% of the participants were diagnosed of having KL grading 3 or 4. Plasma β-endorphin was correlated inversely with the WOMAC subscale of stiffness (r=-0.286, p=0.0311), but no correlation was noted with the WOMAC subscale of pain and physical activity. There was no significant difference of the mean of plasma β-endorphin among the KL gradings. CONCLUSIONS: Plasma β-endorphin is associated with better WOMAC total score and stiffness subscale, but not associated with KL grading of OA. KEYWORDS: knee osteoarthritis, female, β-endorphin, WOMAC, Kellgren-Lawrence
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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 | 0.001 |
| 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".