Vector-Borne Diseases - constant challenge for practicing veterinarians: recommendations from the CVBD World Forum
Bibliographic record
Abstract
The human-animal bond has been a fundamental feature of mankind's history for millennia. The first, and strongest of these, man's relationship with the dog, is believed to pre-date even agriculture, going back as far as 30,000 years. It remains at least as powerful today. Fed by the changing nature of the interactions between people and their dogs worldwide and the increasing tendency towards close domesticity, the health of dogs has never played a more important role in family life. Thanks to developments in scientific understanding and diagnostic techniques, as well as changing priorities of pet owners, veterinarians are now able, and indeed expected, to play a fundamental role in the prevention and treatment of canine disease, including canine vector-borne diseases (CVBDs).The CVBDs represent a varied and complex group of diseases, including anaplasmosis, babesiosis, bartonellosis, borreliosis, dirofilariosis, ehrlichiosis, leishmaniosis, rickettsiosis and thelaziosis, with new syndromes being uncovered every year. Many of these diseases can cause serious, even life-threatening clinical conditions in dogs, with a number having zoonotic potential, affecting the human population.Today, CVBDs pose a growing global threat as they continue their spread far from their traditional geographical and temporal restraints as a result of changes in both climatic conditions and pet dog travel patterns, exposing new populations to previously unknown infectious agents and posing unprecedented challenges to veterinarians.In response to this growing threat, the CVBD World Forum, a multidisciplinary group of experts in CVBDs from around the world which meets on an annual basis, gathered in Nice (France) in 2011 to share the latest research on CVBDs and discuss the best approaches to managing these diseases around the world.As a result of these discussions, we, the members of the CVBD Forum have developed the following recommendations to veterinarians for the management of CVBDs.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.025 | 0.019 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".