Diagnosis of Brucellosis in Livestock and Wildlife
Bibliographic record
Abstract
AIM: To describe and discuss the merits of various direct and indirect methods applied in vitro (mainly on blood or milk) or in vivo (allergic test) for the diagnosis of brucellosis in animals. METHODS: The recent literature on brucellosis diagnostic tests was reviewed. These diagnostic tests are applied with different goals, such as national screening, confirmatory diagnosis, certification, and international trade. The validation of such diagnostic tests is still an issue, particularly in wildlife. The choice of the testing strategy depends on the prevailing brucellosis epidemiological situation and the goal of testing. RESULTS: Measuring the kinetics of antibody production after Brucella spp. infection is essential for analyzing serological results correctly and may help to predict abortion. Indirect ELISAs help to discriminate 1) between false positive serological reactions and true brucellosis and 2) between vaccination and infection. Biotyping of Brucella spp. provides valuable epidemiological information that allows tracing an infection back to the sources in instances where several biotypes of a given Brucella species are circulating. Polymerase chain reaction and new molecular methods are likely to be used as routine typing and fingerprinting methods in the coming years. CONCLUSION: The diagnosis of brucellosis in livestock and wildlife is complex and serological results need to be carefully analyzed. The B. abortus S19 and B. melitensis Rev. 1 vaccines are the cornerstones of control programs in cattle and small ruminants, respectively. There is no vaccine available for pigs or for wildlife. In the absence of a human brucellosis vaccine, prevention of human brucellosis depends on the control of the disease in animals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".