Quantifying within- and between-animal variation and uncertainty associated with counts of <i>Escherichia coli</i> O157 occurring in naturally infected cattle faeces
Bibliographic record
Abstract
Cattle faeces are considered the most important reservoir for human infection with Escherichia coli O157. We have previously described shedding of E. coli O157 in the faeces of naturally infected cattle cohorts. However, the data require further investigation to quantify the uncertainty and variability in the estimates previously presented. This paper proposes a method for analysing both the presence and the quantity of E. coli O157 in cattle faecal samples, using two isolation procedures, one of which enumerates E. coli O157. The combination of these two measurements, which are fundamentally different in nature and yet measuring a common outcome, has necessitated the development of a novel statistical model for ascertaining the contribution of the various components of variation (both natural and observation induced) and for judging the influence of explanatory variables. Most of the variation within the sampling hierarchy was attributable to multiple samples from the same animal. The contribution of laboratory-level variation was found to be low. After adjusting for fixed and random effects, short periods of increased intensity of shedding were identified in individual animals. We conclude that within-animal variation is greater than between animals over time, and studies aiming to elucidate the dynamics of shedding should focus resources, sampling more within than between animals. These findings have implications for the identification of persistent high shedders and for assessing their role in the epidemiology of E. coli O157 in cattle populations. The development of this non-standard statistical model may have many applications to other microbial count 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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".