Social and environmental risk factors for canine leptospirosis: A scoping review
Bibliographic record
Abstract
BACKGROUND: Canine leptospirosis is a zoonotic disease of global importance. The identification of social and environmental risk factors remains limited and is needed for planning interventions. Our objectives were to summarise the global trends in social and environmental risk factors for canine leptospirosis and to identify knowledge gaps. METHODS: We searched peer-reviewed and grey literature for observational studies on canine leptospirosis written in English. Variables assessed via formal statistical tests were recorded and categorised into thematic risk factor groups. The risk factors were summarised by the number of unique studies and their direction of association. RESULTS: Of the 3184 articles identified, 66 met the inclusion criteria. Eight environmental and three social risk factor groups were identified. Exposure to animals and water was commonly studied, but social risk factors were infrequently examined. Most studies were conducted on populations in higher-income countries and countries in the Americas. LIMITATIONS: Several limitations were encountered, including inconsistent study designs and measurement and control of variables as well as disproportionate representation of studies from the Americas and higher-income countries. CONCLUSION: The weight and quality of evidence for social and environmental risk factors were highly variable. Additional research following standard reporting guidelines to improve transparency and increase generalisability to different contexts is needed.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".