Bayesian latent class analysis of <scp>ELISA</scp> and <scp>RT</scp>‐<scp>rPCR</scp> diagnostic accuracy for subclinical <i>Renibacterium salmoninarum</i> infection in Atlantic salmon (<i>Salmo salar</i>) broodstock
Bibliographic record
Abstract
Renibacterium salmoninarum infection causes bacterial kidney disease (BKD) in salmonid freshwater and saltwater life stages, with potentially severe financial loss for the aquaculture industry. Preventing vertical transmission, from infected broodstock to eggs, is key to disease management. As there is no perfect reference standard for detecting R. salmoninarum, we used Bayesian latent class analyses to compare real-time reverse transcriptase PCR (RT-rPCR, mRNA target) and enzyme-linked immunosorbent assay (ELISA; p57 antigen target) diagnostic accuracy for detection in Atlantic salmon broodstock from British Columbia, Canada, and assessed ELISA repeatability. In 2016, 4,544 Atlantic salmon broodstock (no clinical signs of BKD or gross lesions) were sampled for ELISA testing of kidney tissue. Two groups of ELISA positives (n = 132) and two groups of a random sample of ELISA negatives (n = 137) were then tested with RT-rPCR, and ELISA testing was repeated. ELISA testing of broodstock provided the best diagnostic sensitivity (DSe; less chance of false-negative results). The use of joint RT-rPCR and ELISA testing improved DSe over that from each test alone, if a sample was considered positive when either test result was positive. Using these testing schemes in combination with management practices can decrease the likelihood of vertical transmission from subclinically infected broodstock.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".