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Antimicrobial Use and Resistance in Surplus Dairy Calf Production Systems

2022· review· en· W4292259607 on OpenAlexaff
Poonam Gopika Vinayamohan, Samantha R. Locke, R. Portillo-Gonzalez, D.L. Renaud, Gregory Habing

Bibliographic record

VenueMicroorganisms · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAntimicrobialProduction (economics)Antimicrobial stewardshipDairy cattleBusinessAntibiotic resistanceBiotechnologyBiologyEconomicsMicrobiologyAnimal scienceAntibiotics

Abstract

fetched live from OpenAlex

Surplus calves, which consist predominately of male calves born on dairy farms, are an underrecognized source of antimicrobial-resistant (AMR) pathogens. Current production systems for surplus calves have important risk factors for the dissemination of pathogens, including the high degree of commingling during auction and transportation and sometimes inadequate care early in life. These circumstances contribute to an increased risk of respiratory and other infectious diseases, resulting in higher antimicrobial use (AMU) and the development of AMR. Several studies have shown that surplus calves harbor AMR genes and pathogens that are resistant to critically important antimicrobials. This is a potential concern as the resistant pathogens and genes can be shared between animal, human and environmental microbiomes. Although knowledge of AMU and AMR has grown substantially in dairy and beef cattle systems, comparable studies in surplus calves have been mostly neglected in North America. Therefore, the overall goal of this narrative review is to summarize the existing literature regarding AMU and AMR in surplus dairy calf production, highlight the management practices contributing to the increased AMU and the resulting AMR, and discuss potential strategies and barriers for improved antimicrobial stewardship in surplus calf production systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.619
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.059
GPT teacher head0.302
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

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