Bayesian estimation of diagnostic sensitivity and specificity of a qPCR and a bacteriological culture method for <i>Piscirickettsia salmonis</i> in farmed Atlantic salmon (<i>Salmo salar</i> L.) in Chile
Bibliographic record
Abstract
Early detection of piscirickettsiosis is an important purpose of government- and industry-based surveillance for the disease in Atlantic salmon farms in Chile. Real-time qPCRs are currently used for surveillance because bacterial isolation is inadequately sensitive or rapid enough for routine use. Since no perfect tests exist, we used Bayesian latent class models to estimate diagnostic sensitivity (DSe) and specificity (DSp) of qPCR and culture using separate two-test, single-population models for three farms (n = 148, 151, 44). Informative priors were used for DSp (culture (beta(999,1); qPCR (beta(98,2)), and flat priors (beta 1,1) for DSe and prevalence. Models were run for liver and kidney tissues combined and separately, based on the presence of selected gross-pathological signs. Across all models, qPCR DSe was 5- to 30-fold greater than for culture. Combined-tissue qPCR median DSe was highest in Farm 3 (sampled during P. salmonis outbreak (DSe = 97.6%)) versus Farm 1 (DSe = 85.6%) or Farm 2 (DSe = 83.5%), both sampled before clinical disease. Median DSe of qPCR was similar for liver and kidney, but higher when gross-pathological signs were evident at necropsy. High DSe and DSp and rapid turnaround-time indicate that the qPCR is fit for surveillance programmes and diagnosis during an outbreak. Targeted testing of salmon with gross-pathological signs can enhance DSe.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".