Determinants of diabetic nephropathy among diabetic patients in Ethiopia: Systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Diabetic nephropathy (DN) is a long-term kidney disease among diabetic patients. It is the leading cause of end-stage renal failure. In Ethiopia, DN affects the majority of diabetic populations, but there were inconsistent findings about the determinant factors across the studies. METHODS: We have accessed studies using PubMed, Embase, EBSCO, Web of Science, OVID, and search engines including Google and Google Scholar published up to June 2023. The study populations were diabetic patients with nephropathy. The quality of each included article was assessed using the Newcastle-Ottawa quality assessment scale. The odds ratios of risk factors were pooled using a random-effect meta-analysis model. Heterogeneity was assessed using the Cochrane Q statistics and I-Square (I2). The publication bias was detected using the funnel plot and/or Egger's test (p< 0.05). Trim and fill analysis was carried out to treat the publication bias. The protocol has been registered with the reference number CRD42023434547. RESULTS: A total of sixteen articles were used for this reviewed study. Of which, eleven articles were used for advanced age, ten articles for duration of diabetic illness, ten articles for poor glycemic control, and eleven articles for having co-morbid hypertension. Diabetic patients with advanced age (AOR = 1.11, 95% CI: 1.03-120, I2 = 0.0%, p = 0.488), longer duration of diabetic illness (AOR = 1.23, 95% CI = 1.05-1.45, I2 = 0.0%, p = 0.567), poor glycemic control (AOR = 2.57, 95% CI: 1.07-6.14; I2 = 0.0%, p = 0.996), and having co-morbid hypertension (AOR = 4.03, 95% CI: 2.00-8.12, I2 = 0.0%, p = 0.964) were found to be factors associated with DN. CONCLUSIONS: The findings of the study revealed that diabetic patients with advanced age, longer duration of diabetic illness, poor glycemic control status, and co-morbid hypertension were the determinant factors of DN. Therefore, treatment of co-morbid hypertension and high blood glucose and regular screening of renal function should be implemented to detect, treat, and reduce the progression of DN. Furthermore, healthcare workers should give due attention to diabetes with advanced age and a longer duration of diabetes illness to prevent the occurrence of DN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".