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Comparing individual antimicrobial resistant and multi-drug resistant Salmonella enterica across serotypes, sampling sources, sampling periods, and food animal types in the United States (2014–2018)

2023· article· en· W4386190013 on OpenAlexaff
Babafela Awosile, Md. Kaisar Rahman, Gizem Levent, Yesica Botero, Samuel Ajulo, Rasaq A. Ojasanya, Ryan Blake Williams, Guy H. Loneragan

Bibliographic record

VenuePreventive Veterinary Medicine · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSalmonellaSalmonella entericaSerotypeAntimicrobialAntibiotic resistanceBiologyDrug resistanceMultiple drug resistanceVeterinary medicineMicrobiologyAntibioticsMedicineBacteria

Abstract

fetched live from OpenAlex

This study aimed to compare the antimicrobial-resistant Salmonella enterica profiles from three sampling sources cecal contents, HACCP (during processing), and retail meat using phenotypic antibiotic susceptibility and serotype data gathered from 2014 and 2018. Antimicrobial resistance data for 29 major Salmonella serotypes from three sampling sources and associated food animal types (cattle, swine, chicken, and turkey) were obtained from the database of the United States National Antimicrobial Resistance Monitoring System. Using multivariable logistic regression models, we compared individual and multi-drug resistance (MDR) in Salmonella enterica between the three sampling sources, food animal types, sampling period, and Salmonella serotypes. Across the three sources and throughout the sampling period, the recovery of antimicrobial-resistant Salmonella enterica - including MDR, MDR-AmpC, and ACSSuT - among food animal types were dependent on the sampling period and, in some cases, sampling sources and period for the selected antimicrobials. The predicted probability of antimicrobial resistance was greater in Salmonella serotypes from turkey compared to other food animal types, conditional on sampling sources. Ceftriaxone-resistant (OR=0.83, 95% CI: 0.69-0.99), and Sulfisoxazole-resistant (OR=0.84, 95% CI: 0.72-0.98) Salmonella serotypes were less likely to be recovered from the Hazard Analysis and Critical Control Point (HACCP) sources than with the cecal sources. Except for Salmonella serotypes Dublin and Newport, most of the Salmonella serotypes were less likely to be resistant to the selected antimicrobials, or found as MDR, compared to serotype Typhimurium. This study offers an integrated view on the predicted probability of MDR Salmonella serotypes, as well as insights into which serotypes are persistent, emerging or declining across sampling sources and food animal types in the United States.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.511
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.146
GPT teacher head0.332
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2023
Admission routes1
Has abstractyes

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