Meta-analysis of risk factors for carotid plaque formation in type 2 diabetes mellitus
Bibliographic record
Abstract
Objective: To investigate the risk factors of carotid artery plaque formation in type 2 diabetes mellitus in China through meta-analysis. Methods: The literature on the risk factors of carotid plaque formation in type 2 diabetes mellitus in China was retrieved from China Journal Full-text Database (CNKI), Wanfang Digital Journal Full-text Database (Wanfang), VIP journal Resource integration Service platform (VIP) and PubMed database. Newcastle-Ottawa Scale(NOS) used the most comprehensive case-control trial data collection to evaluate the quality of the literature excerpts combined with inclusion and exclusion criteria.Literatures with scores ≥7 were included in the study, and finally meta-analysis was conducted by RevMan5.4. Results: 32 literatures met the inclusion criteria, and the cumulative number of cases and controls were 5710 and 5405, respectively. The results of meta-analysis showed that the risk factors of carotid plaque formation in type 2 diabetes mellitus in China were as follows: Triglyceride (TG) (OR= 2.74, 95%CI: 2.25-3.33), total cholesterol (TC) (OR=2.50, 95%CI: 2.22-2.83), glycosylated hemoglobin (HbA1c) (OR=1.41, 95%CI:1.33 ~ 1.50), history of hypertension (OR=2.03, 95%CI:1.69 ~ 2.44), low density lipoprotein (LDL-C) (OR=1.64, 95%CI:1.31 ~ 2.06), age (OR=1.12, 95%CI:1.09 ~ 1.14), diabetes duration (OR=1.04, 95%CI:1.03 ~ 1.06), smoking (OR=1.30, 95%CI:1.18 ~ 1.44), systolic blood pressure (SBP) (OR=1.04, 95%CI:1.02 ~ 1.05). Conclusion: TG, TC, HbA1c, hypertension history, LDL-C, age, diabetes course, smoking and SBP are independent risk factors for carotid plaque formation in type 2 diabetes mellitus. Smoking cessation, balanced diet, prevention and active treatment of hypertension have great significance for the formation of carotid plaque in type 2 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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.052 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".