Unmasking the silent epidemic: a comprehensive systematic review and meta-analysis of undiagnosed diabetes in Ethiopian adults
Bibliographic record
Abstract
Background: Undiagnosed diabetes mellitus poses a significant global public health concern, exerting a substantial impact on the well-being of individuals, their families, and societies at large. Those individuals with undiagnosed diabetes miss opportunities to maintain quality of life and prevent diabetes-related complications. Even if there are ample primary studies on undiagnosed diabetes in Ethiopia, the results reveal conflicting results. Therefore, a comprehensive national picture of undiagnosed diabetes is essential for designing effective strategies at the national level. Methods: This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for prevalence studies (PROSPERO ID: CRD42021266676). PubMed, Web of Science and the World Health Organization's Hinari portal were searched using a strategy developed in collaboration with Liberians. The inclusion criteria comprised studies reporting undiagnosed diabetes in Ethiopia. Two independent reviewers conducted a quality assessment using a 10-item appraisal tool. Meta-analysis and meta-regression were performed using a random-effects model. Result: = 75.4). Factors significantly associated with undiagnosed diabetes include age, waist circumference, overweight, family history of diabetes, and a history of hypertension. Conclusion: Our systematic review found a noteworthy prevalence of undiagnosed diabetes in Ethiopia. The majority of factors linked with undiagnosed diabetes in this review were modifiable. This underscores the importance of targeted factors and public health interventions to improve early detection and reduce the burden of undiagnosed diabetes and its complications in Ethiopia. Systematic review registration: https://www.crd.york.ac.uk/prospero, identifier CRD42021266676.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".