Validation of laboratory tests for infectious diseases in wild mammals: review and recommendations
Bibliographic record
Abstract
Evaluation of the diagnostic sensitivity (DSe) and specificity (DSp) of tests for infectious diseases in wild animals is challenging, and some of the limitations may affect compliance with the OIE-recommended test validation pathway. We conducted a methodologic review of test validation studies for OIE-listed diseases in wild mammals published between 2008 and 2017 and focused on study design, statistical analysis, and reporting of results. Most published papers addressed Mycobacterium bovis infection in one or more wildlife species. Our review revealed limitations or missing information about sampled animals, identification criteria for positive and negative samples (case definition), representativeness of source and target populations, and species in the study, as well as information identifying animals sampled for calculations of DSe and DSp as naturally infected captive, free-ranging, or experimentally challenged animals. The deficiencies may have reflected omissions in reporting rather than design flaws, although lack of random sampling might have induced bias in estimates of DSe and DSp. We used case studies of validation of tests for hemorrhagic diseases in deer and white-nose syndrome in hibernating bats to demonstrate approaches for validation when new pathogen serotypes or genotypes are detected and diagnostic algorithms are changed, and how purposes of tests evolve together with the evolution of the pathogen after identification. We describe potential benefits of experimental challenge studies for obtaining DSe and DSp estimates, methods to maintain sample integrity, and Bayesian latent class models for statistical analysis. We make recommendations for improvements in future studies of detection test accuracy in wild mammals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".