<i>Trichomonas vaginalis</i> infection in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
Background An estimated 30 million new cases of Trichomonas vaginalis are recorded annually in sub-Saharan Africa. In Ethiopia, there is no study that systematically compiled the burden of T. vaginalis. Therefore, this study aimed to estimate the pooled prevalence of T. vaginalis in Ethiopia. Methods Electronic databases such as PubMed/Medline, EMBASE, Science Direct, Scopus, HINARI, Google Scholar, and Cochrane Library were systematically searched, and studies with high-quality Newcastle Ottawa Scale scores were included. Analyses were performed using STATA version 14 software, and heterogeneity of studies was assessed using the Cochrane’s Q test statistics and I 2 test statistics. Sub-group, sensitivity analysis, and publication bias were performed. Results Ten eligible studies consisting of 2979 study participants were included. The overall pooled prevalence of T. vaginalis infections in Ethiopia was 9.62%. Sub-group analysis showed that the overall pooled prevalence of T. vaginalis infections in pregnant women and other study groups was 6.68% and 12.86%, respectively. Publication bias was detected by funnel plots and Egger’s tests. Conclusions This study showed that the overall pooled prevalence of T. vaginalis infections was relatively high. This study should trigger policy makers, governmental and non-governmental organizations, and healthcare providers to give attention for prevention and control of T. vaginalis infection.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.013 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".