Bibliographic record
Abstract
AIM: To present a review and to describe the most widely used laboratory tests for serology diagnosis of brucellosis along with their pros and cons. METHODS: Review the recent literature on brucellosis serology diagnostic tests. The choice of the testing strategy depends on the prevailing brucellosis epidemiological situation and the goal of testing. RESULTS: The 'gold standard' for the diagnosis of brucellosis is isolation and identification of the causative bacterium, a member of Brucella sp. Isolation of Brucella sp. requires high security laboratory facilities (biological containment level 3), highly skilled personnel, an extended turnaround time for results and it is considered a hazardous procedure. Hence brucellosis is generally diagnosed by detection of an elevated level of antibody in serum or other body fluid. This is a presumptive diagnosis as other microorganisms and perhaps environmental factors can also cause increased antibody levels. CONCLUSION: A large number of serological tests for brucellosis have been devised over the 100+ years since its initial isolation, starting with a simple agglutination test and progressing to sophisticated primary binding assays available today. However, no test devised to date is 100% accurate so generally serological diagnosis consists of testing sera by several tests, usually a screening test of high sensitivity, followed by a confirmatory test of high specificity.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".