Diagnosis of Brucellosis
Bibliographic record
Abstract
Brucellosis is an important zoonosis and a significant cause of reproductive losses in animals.Abortion, placentitis, epididymitis, and orchitis are the most common clinical manifestations in animals.In humans, brucellosis is a debilitating and chronic disease, which may affect a variety of organs.Clinical diagnosis of brucellosis is not easily achieved.Laboratory testing is therefore very important for a correct identification of the disease in humans and for the detection and confirmation in animals.Definitive diagnosis is normally done by isolation and identification of the causative agent.While definitive, isolation is time-consuming, must be performed by highly skilled personnel, and it is hazardous.For these reasons, serological tests are normally preferred.Brucellosis serology have advanced considerably in the last decades with very sensitive and specific new tests available.Modern genetic characterization of Brucellae using molecular DNA technology have been developed.Several PCR-based assays have been proposed, from the rapid recognition of genus to differential identification of species and strains.This review describes bacteriological, serological, and molecular methods used for the diagnosis of human and animal brucellosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".