Studies on the prevalence of blindness in Ethiopia: a protocol for the systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Blindness refers to a lack of vision and/or 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 However, the study on the magnitude of blindness in Ethiopia was outdated, that means it was conducted in 2005-2006. Therefore, this protocol has been proposed to estimate the pooled prevalence of blindness in Ethiopia to provide up-to-date, comprehensive evidence on this theme. METHODS AND ANALYSIS: The following databases will be used to search articles: PubMed, Cochrane Library, Google Scholar and retrieving references. Standard data extraction approach will be employed and presented using Preferred Reporting Items for Systematic Review and Meta-Analysis. The Newcastle-Ottawa Scale quality assessment tool will be used to evaluate the quality of studies. Analysis will be held using STATA V.11. Funnel plot and Egger's regression test will be applied to check for the potential sources of bias. Heterogeneity among the studies will be tested using Higgins method in which I² statistics will be calculated and compared with the standard. Meta-regression and subgroup analysis will be done to identify the potential sources of heterogeneity. Cross-sectional and survey studies conducted in Ethiopia and published in English language will be included. ETHICS AND DISSEMINATION: Ethics approval and consent are not required. On completion, the result will be submitted to a reputable peer-reviewed journal. TRIAL REGISTRATION NUMBER: CRD42021268448.
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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 | 0.076 | 0.112 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.021 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".