Prevalence of Fascioliasis in Livestock and Humans in Pakistan: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Fascioliasis is a parasitic infection that affects both livestock and humans. Understanding the distribution of Fasciola spp. can help the development of preventive measures to control fascioliasis. This systematic review and meta-analysis aimed to estimate the status of fascioliasis among livestock and humans in Pakistan between 2000 and 2020. Based on the selection criteria, 25 articles were selected from Google Scholar, PubMed, and Scopus. This review included 76,099 animals, including 13,738 that were positive for fascioliasis. The overall prevalence was 18.1%; it was 0.3% in humans and 20.1% in livestock. Among animal hosts, the prevalence was highest in sheep (53.5%), followed by the goats (44.9%), cows (21.3%), buffaloes (16.8%), cattle (12.7%), and humans (0.3%). Sindh had the highest prevalence at 42.7%, followed by Baluchistan (25.2%), Punjab (17.7%), Khyber Pakhtunkhwa (10.7%), and Islamabad capital territory (1.5%). In the Punjab province, sheep had the highest prevalence (65.7%); in Khyber Pakhtunkhwa, it was buffalo (15.9%); and in Baluchistan, it was cows (28.5%). The prevalence of Fasciola spp. was higher in Sindh and Baluchistan than in the other provinces. The presented results are essential for developing preventive approaches for the management of human health and minimizing economic loss in the livestock industry in Pakistan. Preventive-curative treatments two times a year followed by a prophylactic treatment at the end of the dry season are crucial throughout the areas of Pakistan that serve as hotspots for infection by Fasciola sp. For humans, regular, prioritized surveys must be performed for high-risk populations so that the real situation can be assessed and addressed in a timely manner.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".