National Trends in the Prevalence of Diabetic Peripheral Neuropathy Among Diabetes Mellitus Patients in Indonesia (2010–2024): A Pooled Meta-Analysis of 46 Studies with 2,808 Participants
Bibliographic record
Abstract
Background: Diabetic peripheral neuropathy (DPN) is a prevalent and debilitating complication of diabetes mellitus that significantly impacts quality of life and increases morbidity. Indonesia faces a rapidly growing diabetes burden; however, national-level estimates of DPN prevalence remain limited. Understanding the epidemiological patterns of DPN is critical to guide screening strategies, clinical management, and region-specific health interventions. Purpose: This study estimated, on a national basis, the prevalence of DPN among diabetic patients in Indonesia during the period 2010 to 2024. Methods: A meta-analysis was conducted using data sources and a comprehensive search of PubMed, Embase, Scopus, Web of Science, CINAHL Plus, and Google Scholar for relevant articles up to January 2025. A generalized mixed model was employed to analyze the pooled prevalence under the assumption of random effects. The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS), and Heterogeneity was assessed through I² statistics and Cochran's Q tests. Results: The sample comprised 46 studies with 2808 participants. The prevalence of DPN was 76.65% (95% CI 64.82-85.40) based on the Random Effects Model. High prevalence was found in patients aged 40-60 with type 2 diabetes in institutional settings and moderate diabetes duration. Conclusion: The preponderance of DPN in Indonesia is tremendously high and differs across regions and demographics. Early detection and resources in situ are important for proper management. PROSPERO registration number: CRD42025643949
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.062 |
| Bibliometrics | 0.009 | 0.009 |
| 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".