Pooled prevalence of blindness in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Blindness is defined as presenting visual acuity worse than 3/60 in the better eye. Its highest proportion has been conforming to the developing countries such as Ethiopia. So, timely information is crucial to design strategies. However, the study on the magnitude of blindness in Ethiopia was outdated which means it was conducted in 2005-2006. Therefore, this study was proposed to estimate the pooled prevalence of blindness in Ethiopia.Databases like PubMed, Cochrane library, Google Scholar and references of retrieved articles were used to search for articles. A standard data extraction approach was employed and presented using Preferred Reporting of Items for Systematic Reviews and Meta-Analyses. The Newcastle-Ottawa Scale quality assessment tool was used to evaluate the quality of studies. Analysis held using STATA V.11. The funnel plot and Egger's regression test were applied to check for the potential sources of bias. Heterogeneity among the studies was tested using I² statistics that have been calculated and compared with the standard. Meta-regression and subgroup analysis were done to identify the potential sources of heterogeneity. Estimation of blindness was carried out using Duval and Twee die's trim and fill analysis. The pooled prevalence of blindness in Ethiopia is found to be 1.18% (95% CI 0.650% to 1.706%). Blindness is among the main public health difficulties in Ethiopia. So, it demands up-to-date strategies and its implementation, preventive and curative eye care service with affordable and accessible interventions, and evidence-based advocacy. The trial Registration Number is CRD42021268448.
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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.054 |
| Bibliometrics | 0.012 | 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) | 0.003 | 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".