ESTIMATION OF SENSITIVITY AND SPECIFICITY OF CULTURE AND DANISH-MIX ELISA FOR DETECTION OF
Bibliographic record
Abstract
Purpose Both bacterial culture and serological assays, such as the Danish-mix ELISA for the detection of antibodies, are commonly used as tools for detecting and monitoring Salmonella in swine. The comparison and ultimate interpretation of results are made more difficult due to the absence of a gold standard. In this study, a previously published Bayesian method is adapted and applied to field data from Western Canada in order to determine posterior distributions of the sen-sitivities and specificities of these two tests. Materials and Methods Ten farrow-to-finish swine herds (herd size n>100 sows) from Alberta and Saskatchewan were selected by swine veterinarians, based on presumed Salmonella positive status (n=8) or Salmonella negative status (n=2). Each herd was sampled once, taking samples from each phase of production (breeding, nursery, grow-finish), however, only the results from the grow-finish phase were used in this analysis. In the grow-to-finish area one pooled pen floor fecal sample and one blood sample were collected from each of 30 pens. Individual fecal samples were also collected from the rectum and matched to the concurrent blood sample. Fecal samples were tested for Salmonella using AAFRD Food Safety Division (FSD) bacterial culture and PCR. One isolate per each Salmonella positive sample was serotyped by Health Canada, Laboratory for
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".