Amendement 2: Exploring Antimicrobial Resistance in Escherichia coli: A Scoping Review Protocol of Antibiotic-free Beef Cattle in Canada and the United States of America.
Bibliographic record
Abstract
Escherichia coli is a gram-negative bacterium commonly found in the human and animal gastrointestinal tract. Beef cattle are considered reservoirs, although some strains can trigger diarrhea in newborn calves. Certain strains, such as Shiga toxin-producing E. coli (STECs), pose health risks in humans and animals [2]. Antibiotic resistance in E. coli is a significant concern in human and veterinary medicine, as it can lead to treatment challenges and transmission to other bacteria [3]. Canada and the United States' Drug-Resistant Index score lie among the lowest ten countries, and this can be associated with the wide use of narrow-spectrum penicillin [4]. Strains encode accessory resistance and are likely resistant to multiple antibiotic classes [5]. Evidence suggests that E. coli can be resistant to more than one antimicrobial drug, and the most common resistance phenotypes are older drugs such as tetracycline, sulfonamide, streptomycin, and ampicillin [6]. Antibiotic-free beef cattle certifications have been implemented to address antimicrobial resistance concerns in the food chain [7]. However, the evidence regarding the prevalence of resistant bacteria in antibiotic-free production systems is limited. Antimicrobial resistance in Escherichia coli represents a significant One Health issue, highlighting the urgent need to explore alternatives, such as limiting the use of antibiotics in beef cattle production to therapeutic treatments, to mitigate the spread of resistant strains and safeguard human and animal health [8].
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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 | 0.093 | 0.164 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.087 | 0.017 |
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".