Omentin-1 in diabetes mellitus: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Previous studies found inconsistent results on the relationship between diabetes mellitus and concentrations of omentin-1. This study performed a systematic review and meta-analysis to summarize previous findings on this relationship. METHODS: Studies related to this outcome were obtained using a systematic search in the electronic databases of Cochrane Library, PubMed, Embase, SCOPUS, Google Scholar, gray literature and Web of Science in September 2019. The random effects model was used to measure the strength of the association between diabetes mellitus and concentrations of omentin-1, using standardized mean difference. RESULTS: Forty-two eligible studies were included in the final meta-analysis. There was no significant difference in omentin-1 concentration between patients with type 1 diabetes mellitus and the controls. On the other hand, lower concentration levels of omentin-1 were observed in patients with gestational diabetes mellitus (standardized mean difference:-0.44, 95% confidence interval:-0.76; -0.12, p = 0.007), or type 2 diabetes mellitus (standardized mean difference: -1.74, 95% confidence interval: -2.31; -1.16, p< 0.001) than in the controls. CONCLUSION: Decreased omentin-1 concentrations may be an important indicator for gestational diabetes mellitus and type 2 diabetes mellitus. More studies are needed to validate this hypothesis and evaluate the role of omentin-1 concentrations in type 1 diabetes mellitus.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".