Avances de las pruebas únicas para detectar la brucelosis
Bibliographic record
Abstract
This paper describes an indirect enzyme-linked immunosorbent assay (I-ELISA) and a fluorescence polarisation assay (FPA), each capable of detecting antibody in several species of hosts to smooth and rough members of the genus Brucella. The I-ELISA uses a mixture of smooth lipopolysaccharide (SLPS) and rough lipopolysaccharide (RLPS) as the antigen, and a recombinant protein A/G conjugated with horseradish peroxidase as the detection reagent. When using individually determined cutoff values, the SLPS/RLPS combined-antigen I-ELISA detected antibody in slightly more animals exposed to SLPS or to RLPS than did I-ELISA procedures using each individual antigen separately. Similarly, the assay using combined antigens detected antibody in slightly fewer animals not exposed to Brucella sp. When a universal cutoff of 10% positivity was used (relative to strongly positive control sera of each species), the overall performance index (percentage sensitivity plus percentage specificity) value decreased by 1.0 (from 199.4 to 198.4). In the FPA, it was not possible to use a universal cutoff without significant loss of performance. The overall sensitivity value for the FPA using the combined FPA antigen was 1.0% lower than using the O-polysaccharide (OPS) from SLPS and 9.1% higher than using the core antigen (CORE) from RLPS. When the combined antigen was used, the FPA specificity was slightly higher (1.2%) than from only the OPS, and considerably higher (12.6%) than the CORE. Overall, both the I-ELISA and the FPA with combined antigens were suitable as screening tests for all species of Brucella in the animal species tested.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".