Serum Adiponectin Level in Different Stages of Type 2 Diabetic Kidney Disease: A Meta-Analysis
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Background: Biomarkers in predicting the stages of nephropathy associated with type 2 diabetes mellitus are urgent, and adiponectin may be a promising biomarker. This meta-analysis examined the association of serum adiponectin level with the stages of type 2 diabetic nephropathy. Methods: Databases including PubMed, Cochrane Library, EMBASE, China National Knowledge Infrastructure (CNKI), and Wan Fang were searched for published studies on adiponectin and type 2 diabetic kidney disease. The Newcastle-Ottawa scale was used to assess the quality of the literature. STATA 14.0 was used to conduct the statistical analysis. Results: Thirty-four studies with 5254 patients were included in this meta-analysis. The results of this study show that there was no significant difference in serum adiponectin level between normoalbuminuria and the control group (mean difference = -0.42, 95% CI [-1.23, 0.40]), while serum adiponectin level was positively correlated with the severity of type 2 diabetic kidney disease. The serum adiponectin level in type 2 diabetic kidney disease patients ranks as macroalbuminuria > microalbuminuria > normoalbuminuria. Conclusions: Serum adiponectin level might be an important marker to predict the progression of type 2 diabetic kidney disease.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".