Comparison of Escherichia coli Recovery and Antimicrobial Resistance in Cecal, Colon, and Fecal Samples Collected from Wild House Mice (Mus musculus)
Bibliographic record
Abstract
Escherichia coli isolated from colon, cecal, and fecal samples are commonly used as indicator organisms to monitor antimicrobial resistance (AMR) in wild animals. The effect of sample type on E. coli recovery and AMR detection is unknown. We compared the prevalence of E. coli, the frequency of AMR, and the level of agreement between cecal, colon, and fecal samples collected from individual wild house mice (Mus musculus). Samples were collected from 49 mice trapped at swine farms, May-October 2008. We found no difference in the proportion of E. coli-positive samples (82%) among sample types and the agreement among sample types for E. coli recovery ranged from moderate to almost perfect. The percentage of E. coli positive samples resistant to one or more antimicrobial varied from 53% for colon samples to 71% for cecum samples; however, there was no significant difference in the proportion of resistant samples among sample types. The agreement among sample types for resistance to one or more antimicrobial ranged from fair to substantial. These findings indicate that there is no definitive sample type for studies of AMR in house mice. However, we suggest that fecal samples, which have direct contact with the environment, are likely the best sample to use in studies assessing the potential impact of AMR in wildlife on environmental and public health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".