Seroprevalence and trend of human immunodeficiency virus among blood donors in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Blood transfusion is one of the routine therapeutic interventions in hospitals that can be lifesaving. However, this intervention is related to several transfusion-related infections. The human immunodeficiency virus (HIV) is one of the major public health problems associated with blood transfusion. The objective of this systematic review and meta-analysis is to estimate seroprevalence and trend of human immunodeficiency virus among blood donors in Ethiopia. METHODS: Studies on the prevalence of human immunodeficiency virus among blood donors published until 2017 were accessed by conducting a detailed search on PubMed, Cochrane Library, Google Scholar, EMBASE and CINAHL databases using the keywords:-"Seroprevalence" AND "trend" AND "HIV" OR "human" AND "immunodeficiency" AND "virus" OR "human immunodeficiency virus" AND "blood donors" OR "blood donors" OR "Ethiopia". The quality of each article was assessed using a modified version of the Newcastle-Ottawa Scale. Meta-analysis was carried out using a random-effects method. All statistical analyses were done using STATA version 11 software. RESULT: The estimated pooled seroprevalence of human immunodeficiency virus among blood donors in Ethiopia was 2.69% (95% CI (1.79-3.58%)). The overall seroprevalence of human immunodeficiency virus infection showed a significant decline trend from 2004 to 2016. CONCLUSION: The overall seroprevalence of human immunodeficiency virus among blood donors in Ethiopia was high. Routine screening of donor blood for transfusion-transmissible infections is essential for ensuring the safety of blood transfusion.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| 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".