Generalizability and comparability of prevalence estimates in the wild bird literature: methodological and epidemiological considerations
Bibliographic record
Abstract
Wild birds have been the focus of a great deal of research investigating the epidemiology of zoonotic bacteria and antimicrobial resistance in the environment. While enteric pathogens (e.g. Campylobacter, Salmonella, and E. coli O157:H7) and antimicrobial resistant bacteria of public health importance have been isolated from a wide variety of wild bird species, there is a considerable variation in the measured prevalence of a given microorganism from different studies. This variation may often reflect differences in certain ecological and biological factors such as feeding habits and immune status. Variation in prevalence estimates may also reflect differences in sample collection and processing methods, along with a host of epidemiological inputs related to overall study design. Because the generalizability and comparability of prevalence estimates in the wild bird literature are constrained by their methodological and epidemiological underpinnings, understanding them is crucial to the accurate interpretation of prevalence estimates. The main purpose of this review is to examine methodological and epidemiological inputs to prevalence estimates in the wild bird literature that have a major bearing on their generalizability and comparability. The inputs examined here include sample type, microbiological methods, study design, bias, sample size, definitions of prevalence outcomes and parameters, and control of clustering. The issues raised in this review suggest, among other things, that future prevalence studies of wild birds should avoid opportunistic sampling when possible, as this places significant limitations on the generalizability of prevalence data.
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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 | 0.285 | 0.547 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".