Correlation between uric acid levels and bone mineral density in patients with type 2 diabetes mellitus: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose To explore the controversial relationship between uric acid (UA) levels and bone mineral density (BMD) in patients with type 2 diabetes mellitus (T2DM). Patients and methods The PubMed, Embase, and Cochrane Library databases were searched using keywords and related words. Study quality was evaluated using the Newcastle-Ottawa Scale. Studies retrieved in the literature search were systematically screened to extract information and data based on predefined inclusion and exclusion criteria. RevMan version 5.3 and Stata Release 13.0 were used for statistical analysis. Results are expressed as mean difference (MD) and corresponding 95% confidence interval (CI). Heterogeneity was evaluated using the I2 and Q tests. Results This meta-analysis included 10 studies comprising 5,717 patients with T2DM. Study quality ranged from moderate to high. Results of comparative analyses were as follows: normal BMD versus (vs.) osteoporosis (OP) in females, MD −13.83 μmol/L (95% CI −41.69 to 14.03); I2 = 7%; P=0.30); normal BMD vs. osteopenia in females, MD −12.41 μmol/L (95% CI −37.81 to 12.99; I2 = 0%; P=0.92); normal BMD vs. abnormal BMD (osteopenia/OP), MD −23.82 μmol/L (95% CI −33.50 to −14.13; I2 = 0%; P=0.44); and osteopenia vs. OP, MD −22.35 μmol/L (95% CI −29.55 to −15.15; I2 = 5%; P=0.39). No publication bias was observed. Conclusion Compared with normal BMD, abnormal BMD (osteopenia/OP) was associated with lower UA levels. Compared with osteopenia, OP also showed lower UA. Systematic review registration:
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".