Adiponectin: is it a biomarker for assessing the disease severity in knee osteoarthritis patients?
Bibliographic record
Abstract
AIM: The results of previous studies regarding the role of adiponectin in the pathogenesis of osteoarthritis (OA) are controversial. The aim of this study is to investigate the relation of plasma adiponectin levels with clinical and radiological disease severity in knee OA patients. METHOD: Sixty patients with knee OA and 25 healthy controls were included in the study. Patients were divided into two subgroups: lean (Group 1, n = 30) and obese (Group 2, n = 30). Healthy controls were accepted as Group 3 (n = 25). Pain intensity was measured with a visual analogue scale (VAS), functional disability with Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Quality of Life (QoL) with Short Form-36 (SF-36). Also all patients were radiologically evaluated and graded according to Kellgren-Lawrence (KL) scale. Plasma concentrations of adiponectin levels were measured by enzyme-linked immune-sorbent assay (ELISA). RESULTS: Serum adiponectin levels were higher in OA patient subgroups than those in the control group but the difference did not reach a significant level after adjustments for age, gender and body mass index (P = 0.078). There was a positive correlation between adiponectin concentration and KL grading scores. Additionally, there was a positive correlation between adiponectin levels and clinical variables (VAS and WOMAC total scores) in patient subgroups (r = 0.326 P = 0.012, r = 0.583 P < 0.001, respectively). SF-36 scores were inversely associated with adiponectin levels. CONCLUSION: Plasma adiponectin concentrations were associated with both clinical and radiological disease severity in knee OA patients. Thus, adiponectin hormone might be a potential clinically useful biomarker while assessing disease severity in the future.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".