Bayesian latent class model estimates of diagnostic accuracy for three test methods designed to detect spring viremia of carp virus
Bibliographic record
Abstract
Spring viremia of carp virus (SVCV) causes a systemic hemorrhagic disease that poses a significant risk to wild and cultured fish and is listed as notifiable by the World Organization for Animal Health. Validated molecular diagnostic tools for SVCV are required to accurately describe and analyze the ecology of the virus. Here, the diagnostic specificity (DSp) and sensitivity (DSe) (i.e. accuracy) of three SVCV diagnostic tests - 2 reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays Q1G and Q2N and virus isolation by cell culture (VI) - were evaluated using 2-class latent class models run in maximum likelihood (ML) and Bayesian frameworks. Virus-free or experimentally-infected koi were sorted into three populations with low, moderate or high prevalence levels of SVCV (n = 269 fish in total). Koi kidney tissues were tested using Q2N and Q1G and for the VI assay, pools of kidney, spleen and gill tissues were used. All samples were blinded and analyzed in one laboratory. The ML and Bayesian approaches successfully estimated the diagnostic accuracy of the 3 tests with the exception of 1 ML model. The estimates were consistent across the two frameworks. The DSe estimates were higher for Q1G (>98 %) and Q2N (>96 %) compared to VI (>60 %). The DSp of all three tests varied by 12-15 % (79-91 % for Q1G, 79-94 % for Q2N and 81-97 % for VI) across same-fish samples revealing the potential range in test performance for one sample. The 3 fish populations had distinct SVCV prevalence levels estimated at 0-3 % (low), 70-73 % (moderate) and 95-96 % (high). The Bayesian covariance models revealed minor DSe dependence between Q1G and Q2N. The results suggested that SVCV diagnostic tests Q2N and Q1G are suitable for use as diagnostic assays and are fit for presumptive diagnosis, surveillance, and certification of populations or individuals as SVCV free.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".