Prevalence and risk factors for diabetic retinopathy in Nigeria
Bibliographic record
Abstract
Introduction: The prevalence of diabetes mellitus and its complications is rising globally. Diabetic retinopathy is one of the most common microvascular complications of diabetes and is the most common cause of blindness in adults globally. The aims of this study were to determine the pooled prevalence of diabetic retinopathy in Nigeria and to identify the associated risk factors. Methods: Medical databases including PubMed, Google Scholar, African Journals online, Cochrane library, EMBASE, and SCOPUS were searched for studies on diabetic retinopathy in Nigeria between the years 2000 and 2020 using the MESH terms “diabetic retinopathy,” “prevalence,” “risk factors,”, “Nigeria.” The gray literature was also searched. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were strictly adhered to in selecting the studies. The outcome variables of interest were prevalence of diabetic retinopathy in Nigeria as well as risk factors associated with diabetic retinopathy. The Newcastle-Ottawa scale was used to assess the quality and bias of the selected studies. Statistical analyses were performed using Stata version 14.3. DerSimonian and Laird random-effect model was used. Heterogeneity was assessed using the I 2 statistic. Results: Twenty studies met the eligibility criteria and they were selected for the studies. The total sample size was 3299. I 2 statistic was 99%, which suggests a high level of heterogeneity among the selected studies. Using the random-effect model, the pooled prevalence of diabetic retinopathy in Nigeria was 21.3% (95% confidence interval 21.1–21.5). The most common risk factors for diabetic retinopathy in Nigeria were duration of diabetes, poor glycemic control, and hypertension. Conclusion: The prevalence of diabetic retinopathy in Nigeria is high and there is a need to improve the glycemic control of patients with diabetes so as to prevent or delay its onset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".