Low Plasma Adiponectin as a Potential Biomarker for Osteonecrosis of the Femoral Head
Bibliographic record
Abstract
OBJECTIVE: To examine whether plasma adiponectin level is correlated with osteonecrosis of the femoral head (ONFH). METHODS: Blood adiponectin level in patients with nontraumatic ONFH (n = 120) was compared with a group of healthy subjects (n = 120). Patients with hip osteoarthritis (OA; n = 30) and traumatic ONFH (n = 45) were included as controls. Potential compounding factors, such as plasma low-density lipoprotein (LDL), high-density lipoprotein (HDL), apolipoprotein A1 (apo A1), apolipoprotein B (apo B), total cholesterol (TC), triglycerides (TG), and C-reactive protein (CRP) were also examined. RESULTS: Patients with nontraumatic ONFH had significantly lower plasma levels of adiponectin than the healthy controls (7.14 ± 3.53 vs 10.93 ± 3.41 μg/ml, respectively; p < 0.001). Adiponectin level was positively correlated with HDL (r = 0.282, p < 0.001) and age (r = 0.145, p = 0.01), yet negatively correlated with body mass index (r = -0.70, p < 0.001), TG (r = -0.55, p<0.001), LDL/HDL ratio (r = -0.173, p = 0.002), and CRP (r = -0.634, p < 0.001). No correlation was seen with LDL (r = -0.017, p = 0.762). A multiple logistic regression analysis revealed that adiponectin level is an independent predictor of the presence of nontraumatic ONFH (p < 0.001, OR 0.676, 95% CI 0.546 to 0.845). CONCLUSION: Low adiponectin level is significantly associated with the presence of nontraumatic ONFH. This biomarker may be useful in assessing the potential presence of nontraumatic ONFH.
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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) | 0.001 | 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".