The ComparIson Of Brucella Gel AgglutInatIon Test WIthother SerologIcal Tests For The DIagnosIs Of BrucellosIs
Bibliographic record
Abstract
Aim: Brucellosis, a zoonotic disease, may affect several body systems and cause various clinical manifestations considering the infection sites. Diagnosis of brucellosis is mainly based on culture and serological methods, specifically Rose Bengal agglutination (RBT) and standard tube agglutination tests (STA). When RBT, which is usually used as a screening method, is positive for Brucella antigens, STA is prefered to detect an agglutination titre by using serial dilutons of serum sample. On the otherhand, false negative results can be obtained by STA due to the presence of blocking antibodies. The refore, these two methods may be in adequate for diagnosis of Brucellosis. The aim of this study is to compare a novel method, Brucella Coombs gel test (BCGT) with other four serological methods, RBT, STA, Brucella immune-capture agglutination test (BCAP), and Brucella ELISA IgG, IgM tests. Patients and Methods: Serum samples taken from 100 patients, admitted from various clinics at Konya Training and Research Hospital were sent to the Medical Microbiology Laboratory with a clinical diagnosis of Brucellosis. Each serum sample was studied with RBT (Seromed Laboratory Products, Turkey), STA (Biomedica, Canada), BCAP (Brucellacapt, Vircell SD Spain), BCGT (ODAK, ISLAB, Turkey), and ELISA Brucella IgG, IgM (Euroimmune, Germany) methods. All procedures were carried out in accordance with the manufacturer’s recommendations. Results: The rates of positive results for each method were as follows: BCGT 88 (88.0%), RBT 74 (74.0%), STA 56 (56.0%), BCAP 84 (84.0%) and Brucella IgG, IgM test 92 (92.0%). Conclusion: Statistical analysis could not be executed due to lack of a gold standard method in the study. Yet, BCGT provided faster results than STA and BCAP methods because it does not require a 24-hourincubation. This novel test, BCGT, also showed an advantagein the diagnosis of Brucellosis because of detecting blocking antibodies. More comprehensive studies are needed to be performed to confirm the results.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".