The performance of the interferon gamma assay when used as a diagnostic or quality assurance test in Mycobacterium bovis infected herds
Bibliographic record
Abstract
There are two different contexts in the Irish bTB eradication programme in which the interferon-gamma assay (IFN-γ) is applied. Firstly, the IFN-γ assay is applied routinely to high risk cohorts in herds with four or more reactors to the SICTT. The IFN-γ test is then carried out on blood samples submitted to the laboratory within 8h of collection (diagnostic testing). Secondly, the use of the IFN-γ assay has recently been extended to test SICTT reactors as part of a general quality assurance (QA) scheme to monitor the performance of the SICTT. Blood samples from reactors are tested one day after blood collection (QA testing). In this study, we analysed the relative performance of the SICTT and IFN-γ when used in parallel as an 8h diagnostic test and as a 24h QA test on SICTT reactors. A total of 17,725 IFN-γ tests were included in the analysis (11,658 diagnostic tests and 6067 QA tests). Of the samples submitted for diagnostic testing, the proportion positive to IFN-γ decreased with the severity of interpretation of the SICTT result. Of the standard reactors that were tested with IFN-γ in the QA programme, 92.2% were positive to the IFN-γ test. Among animals that were SICTT -ve/IFN-γ +ve, 18.9% were positive at post-mortem compared to 11.8% of those that were SICTT +ve (standard reactor)/IFN-γ -ve. These results highlight the risk associated with retaining SICTT -ve/IFN-γ +ve animals, and suggest that prompt removal of these animals is necessary to reduce the potential for future transmission.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".