Obesity-Related Adipokines Predict Patient-Reported Shoulder Pain
Bibliographic record
Abstract
BACKGROUND/AIMS: Increasingly, an inflammatory modulating effect of adipokines within synovial joints is being recognized. To date, there has been no work examining a potential association between the presence of adipokines in the shoulder and patient-reported outcomes. This study undertakes an investigation assessing these potential links. METHODS: 50 osteoarthritis patients scheduled for shoulder surgery completed a pre-surgery questionnaire capturing demographic information including validated, patient-reported function (Disabilities of the Arm, Shoulder, and Hand questionnaire) and pain (Short Form McGill Pain Questionnaire) measures. Synovial fluid (SF) samples were analyzed for leptin, adiponectin, and resistin levels using Milliplex MAP assays. Linear regression modeling was used to assess the association between adipokine levels and patient-reported outcomes, adjusted for age, sex, BMI, and disease severity. RESULTS: 54% of the cohort was female (n = 27). The mean age (SD) of the sample was 62.9 (9.9) years and the mean BMI (SD) was 28.1 (5.4) kg/m(2). From regression analyses, greater SF leptin and adiponectin levels, but not regarding resistin, were found to be associated with greater pain (p < 0.05). Adipokine levels were not associated with functional outcome scores. CONCLUSIONS: The identified association between shoulder-derived SF leptin and adiponectin and shoulder pain is likely explained by the pro-inflammatory characteristics of the adipokines and represents potentially important therapeutic targets.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".