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Record W2019141915 · doi:10.1186/1746-6148-9-216

Methodological comparisons for antimicrobial resistance surveillance in feedlot cattle

2013· article· en· W2019141915 on OpenAlex
Katharine M. Benedict, Sheryl Gow, Sylvia Checkley, Calvin W. Booker, Tim A. McAllister, Paul S. Morley

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC Veterinary Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsAlberta Health ServicesUniversity of LethbridgeUniversity of CalgaryUniversity of SaskatchewanPublic Health Agency of Canada
FundersAlberta Beef ProducersBeef Cattle Research CouncilPublic Health AgencyPublic Health Agency of Canada
KeywordsAntibiotic resistanceAntimicrobialVeterinary medicineFeedlotBiotechnologyBiologyMedicineMicrobiologyAntibioticsAnimal science

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to objectively compare methodological approaches that might be utilized in designing an antimicrobial resistance (AMR) surveillance program in beef feedlot cattle. Specifically, four separate comparisons were made to investigate their potential impact on estimates for prevalence of AMR. These included investigating potential differences between 2 different susceptibility testing methods (broth microdilution and disc diffusion), between 2 different target bacteria (non-type-specific E. coli [NTSEC] and Mannheimia haemolytica), between 2 strategies for sampling feces (individual samples collected per rectum and pooled samples collected from the pen floor), and between 2 strategies for determining which cattle to sample (cattle that were culture-positive for Mannheimia haemolytica and those that were culture-negative). RESULTS: Comparing two susceptibility testing methods demonstrated differences in the likelihood of detecting resistance between automated disk diffusion (BioMIC®) and broth microdilution (Sensititre®) for both E. coli and M. haemolytica. Differences were also detected when comparing resistance between two bacterial organisms within the same cattle; there was a higher likelihood of detecting resistance in E. coli than in M. haemolytica. Differences in resistance prevalence were not detected when using individual animal or composite pen sampling strategies. No differences in resistance prevalences were detected in E. coli recovered from cattle that were culture-positive for M. haemolytica compared to those that were culture-negative, suggesting that sampling strategies which targeted recovery of E. coli from M. haemolytica-positive cattle would not provide biased results. CONCLUSIONS: We found that for general purposes, the susceptibility test selected for AMR surveillance must be carefully chosen considering the purpose of the surveillance since the ability to detect resistance appears to vary between these tests depending upon the population where they are applied. Continued surveillance of AMR in M. haemolytica recovered by nasopharyngeal swab is recommended if monitoring an animal health pathogen is an objective of the surveillance program as results of surveillance using fecal E. coli cannot be extrapolated to this important respiratory pathogen. If surveillance of E. coli was pursued in the same population, study populations could target animals that were culture-positive for M. haemolytica without biasing estimates for AMR in E. coli. Composite pen-floor sampling or sampling of individuals per-rectum could possibly be used interchangeably for monitoring resistance in E. coli.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.340
GPT teacher head0.454
Teacher spread0.114 · 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