To pool or not to pool? Guidelines for pooling samples for use in surveillance testing of infectious diseases in aquatic animals
Bibliographic record
Abstract
Samples from multiple animals may be pooled and tested to reduce costs of surveillance for infectious agents in aquatic animal populations. The primary advantage of pooling is increased population-level coverage when prevalence is low (<10%) and the number of tests is fixed, because of increased likelihood of including target analyte from at least one infected animal in a tested pool. Important questions and a priori design considerations need to be addressed. Unfortunately, pooling recommendations in disease-specific chapters of the 2018 OIE Aquatic Manual are incomplete and, except for amphibian chytrid fungus, are not supported by peer-reviewed research. A systematic review identified only 12 peer-reviewed aquatic diagnostic accuracy and surveillance studies using pooled samples. No clear patterns for pooling methods and characteristics were evident across reviewed studies, although most authors agreed there is a negative effect on detection. Therefore, our purpose was to review pooling procedures used in published aquatic infectious disease research, present evidence-based guidelines, and provide simulated data examples for white spot syndrome virus in shrimp. A decision tree of pooling guidelines was developed for use by peer-reviewed journals and research institutions for the design, statistical analysis and reporting of comparative accuracy studies of individual and pooled tests for surveillance purposes.
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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 | 0.221 | 0.345 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".