Unleashing the literature: a scoping review of canine zoonotic and vectorborne disease research in <i>Canis familiaris</i> in North America
Bibliographic record
Abstract
Domestic dogs (Canis familiaris) provide important benefits to human beings but can also transmit pathogens. Information on the breadth of canine zoonoses and vectorborne research in North America is scarce. A scoping review was conducted to examine (1) the number and type of canine zoonoses and vectorborne studies in domestic dogs conducted in North America since the start of the 21st century; (2) the main research methods reported; (3) the Inequality-adjusted Human Development Index (IHDI) countries in which research was conducted; and (4) whether collaborative integrated terminology was reported in objectives or methods sections. Title/abstract screening, full-text screening, and data-charting were completed by two reviewers. We identified 507 publications evaluating 43 zoonotic or vectorborne pathogens in domestic dogs. Most studies (n = 391 of 512 (76.37%)) were conducted in the USA. The five most frequently researched pathogens were Ehrlichia spp. (n = 81 of 507 (15.98%)), Borrelia burgdorferi (n = 64 of 507 (12.62%)), Leptospira spp. (n = 54 of 507 (10.65%)), Rabies virus (n = 42 of 507 (8.28%)), and Influenza viruses (n = 41 of 507 (8.09%)). These pathogens can cause moderate to severe health outcomes in human beings and in dogs irrespective of IHDI ranking; our review highlights important counts of research conduct among North American countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".