Poor glycemic control and associated factors among diabetic patients in Ethiopia; A Systemic review and meta-analysis
Bibliographic record
Abstract
Abstract Introduction The major global public health problems now days are diabetes especially the burden is high in low income countries including Ethiopia due to the limited resource for screening and early diagnosis of the diabetes. To prevent diabetic complications including organ damage and micro vascular complications blood glucose level should be maintained at an optimum level. However there was no pooled national picture on poor glycemic control and its associated factors. Methods Different data base searching engine including PubMed, Google scholar, the Cochrane library, MEDLINE,, HINARY and African journal online (AJOL) were used. The Joanna Briggs Critical Appraisal Tools and Newcastle Ottawa scale for assessing the quality of cross sectional studies were used for quality assessment. The meta-analysis was conducted using STATA 14 software. I 2 statistic and egger weighted regression were used to assess heterogeneity and publication bias. Results A total of 134 studies were identified from different database searching engines and other sources. After removing for duplication, absence of abstract and review of the full text 12 studies were including in the meta-analysis. The pooled prevalence of poor glycemic control among diabetic patients in Ethiopia is 64.72% with 95% confidence interval 63.16-66.28%. The sub group analysis of poor glycemic control among diabetic patients in different region of the country shows consistent and high prevalence of poor glycemic control ranging from 62.5% in Tigray region to 65.6% in Oromia region of the country. Residence, dyslipidemia and diet adherence were significantly association with poor glycemic control among diabetic patients in Ethiopia. Conclusion The prevalence of poor glycemic control among diabetic patients was high in Ethiopia and consistent across different regions of the country. The most important factors associated with poor glycemic factor among diabetic patients were being rural residence, having dyslipidemia and not adhering to dietary plan.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| 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".