Prevalence of prediabetes and associated risk factors in the Eastern Mediterranean Region: a systematic review
Bibliographic record
Abstract
BACKGROUND: Prediabetes increases the risk of diabetes mellitus and complications. The current study was planned to assess the prevalence and risk factors of prediabetes in Eastern Mediterranean Region countries. METHODS: The PRISMA reporting guidelines were followed when reporting this study. Five electronic databases: PubMed, Embase, Scopus, CINAHL, and Web of Science, were searched to identify relevant studies. We included observational studies that used either the American Diabetes Association or World Health Organization prediabetes criteria as definitions for adult populations in any of the Eastern Mediterranean Region countries. We identified 13,851 references, of which 41 were included for data extraction. The Quality Assessment Tool for Cross-Sectional Studies and the Newcastle-Ottawa Scale for other studies were used to assess the quality of the included studies. RESULTS: The overall prevalence of prediabetes ranged from 2.2% to 47.9%; Age, gender, obesity, and high blood pressure were the most reported risk factors in the EMR. Factors like low education, smoking, family history of diabetes, and physical inactivity were associated with prediabetes in some populations. CONCLUSION: The region was found to have a high prevalence of prediabetes, ranking it among regions with the most significant frequency. Modifiable factors such as obesity, hypertension, and inactivity, in addition to age and gender, are among the region's most frequently identified risk factors for prediabetes.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".