Application of surveillance data in evaluation of diagnostic tests for infectious salmon anemia
Bibliographic record
Abstract
Infectious salmon anemia (ISA) is a viral disease in farmed Atlantic salmon Salmo salar, characterized by lethargy, anorexia, anemia and death. Test methods used for regulatory decisions to remove infected cages include the indirect fluorescent antibody test (IFAT), the reverse transcription-polymerase chain reaction test (RT-PCR), and virus isolation (VI). However, no thorough evaluation of these diagnostic tests has been carried out on field samples. The objective of this study was to evaluate the sensitivity and specificity of ISA diagnostic tests as individual tests and in combinations, using data collected by the provincial government surveillance program. Because a 'gold standard' reference test for ISA was not available, cage status was based on clinical disease records. A pool of fish from negative farms that had never had clinical ISA and a pool of fish from positive cages that were experiencing an outbreak of clinical ISA were obtained and assumed to be uninfected and infected respectively. A total of 1071 fish were used in this study. Depending on the test's cut-off value, the sensitivity and specificity for histopathology ranged from 30 to 73 and 72 to 99% respectively. IFAT had sensitivities and specificities in the range of 64 to 83 and 96 to 100% respectively. For the RT-PCR, sensitivity and specificity were 93 and 98% respectively. Test performances were also evaluated in series and in parallel combinations. Sensitivities are maximized when tests are evaluated in parallel, and ranged from 75 to 98%. Specificities are maximized when the tests are evaluated in series, and ranged from 99 to 100%. Current surveillance testing protocols should be reviewed to capitalize on this newly available information on test characteristics.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".