Association of MMP-9 polymorphisms with diabetic nephropathy risk: a protocol for systematic review and meta analysis
Bibliographic record
Abstract
Background: Diabetic nephropathy (DN) is a multifactorial disease with gene-environment interaction resulting in progressive renal function damage. Multiple studies have assessed the association between matrix metalloproteinase-9 (MMP-9) gene promoter polymorphism and DN susceptibility. However, the results are inconclusive. In the present study, we will conduct a meta-analysis to further examine this relationship more precisely. Methods: Electronic databases (Pubmed, Web of Science, Embase, Google Scholar, Wanfang, China Biological Medicine and China National Knowledge Infrastructure) will be used to search clinical case-control studies about MMP-9 polymorphism and DN published until 18 August 2020. The language will be restricted to Chinese and English. Two reviewers will take charge of completing the selection of study, the extraction of data as well as the assessment of study quality independently. The Newcastle-Ottawa Scale will be used to evaluate the study quality. We will evaluate the association under five genetic models. Fixed-effects or random-effects models will be used to calculate the effect sizes of odds ratio (OR) and 95% confidence intervals (95% CI). Afterwards, subgroup analysis will be conducted in terms of the ethnicity and genotyping method. Additionally, sensitivity analysis will be performed via sequentially omitting each of the included studies one at a time. The funnel plots, Egger’s regression test and Begg’s rank correlation test will be used to test the potential publication bias. All the statistical analyses will be performed using Review Manager 5.3 and Stata 12.0. Results: This protocol reported according to the Preferred Reporting ltems for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement. This study will provide a better understanding of the association between MMP-9 polymorphisms and DN risk. Conclusion: Publishing this protocol will minimise the potential bias related to data mining, thus contributing to generation of reliable evidence.
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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 | 0.065 | 0.105 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.027 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.004 |
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".