Prevalence and antimicrobial susceptibility profile of <i>Listeria monocytogenes</i> in humans, animals, and foods of animal origin in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Listeria monocytogenes is a food-borne bacterial pathogen known to cause a burden on human health and food safety globally. Regardless of the few available studies on Listeria monocytogenes, there is no comprehensive evidence of its prevalence and antimicrobial susceptibility in Ethiopia. We conducted to estimate the pooled prevalence and antimicrobial susceptibility profile of Listeria monocytogenes from various sources in Ethiopia. The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Checklist. The quality of the studies was assessed based on the Newcastle–Ottawa scale. We found 18 studies that fit our inclusion criteria. Results were synthesized with random-effects meta-analyses and meta-regressions to evaluate heterogeneity between studies. The pooled prevalence of Listeria monocytogenes from various sources in Ethiopia was 4.2% (95% CI, 2.13, 6.22). The pooled prevalence was higher in human subjects 6.4% (95% CI, 0.27–15.6) when compared to animals 4.7% (95% CI, −0.40 to 9.1) and foods of animal origin 5.1% (95% CI, 0.42–10.6). Higher rate of resistance of Listeria monocytogenes against tetracycline (72.7%), amoxicillin (63.7%), nalidixic acid (63.6%), penicillin (63.6%), and cephalothin (54.5%) was observed. The prevalence of Listeria monocytogenes and the occurrence of resistant isolates in different source populations warn of a potential future threat to public health. Hence, increasing public awareness and designing effective policies and disease control measures are strongly recommended.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.044 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".