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Record W2947895476 · doi:10.1637/11971-091718-reg.1

Antimicrobials Used for the Therapy of Necrotic Enteritis and Coccidiosis in Broiler Chickens and Turkeys in Canada, Farm Surveillance Results (2013–2017)

2019· article· en· W2947895476 on OpenAlexaffabout
Agnes Agunos, Anne Deckert, David Léger, Sheryl Gow, Carolee A. Carson

Bibliographic record

VenueAvian Diseases · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCoccidia and coccidiosis research
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsFlockCoccidiosisCoccidiostatsBroilerVeterinary medicineAntimicrobialContext (archaeology)BiologyPopulationAnimal scienceMedicineEnvironmental healthMicrobiology

Abstract

fetched live from OpenAlex

Since 2013, the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) has collected antimicrobial use (AMU) and antimicrobial resistance data from sentinel broiler chicken flocks (Br, five provinces) and sentinel turkey flocks (Tk, one province 2013–2015, three provinces 2016–2017). The objectives of this paper were to describe various preventive strategies aimed at controlling necrotic enteritis (NE) and coccidiosis in the broiler chicken and turkey flocks participating in CIPARS and FoodNet Canada Farm Surveillance Program between 2013 and 2017, to quantify and identify trends in antimicrobials used in feed, and to describe temporal changes in the diagnoses of bacterial and protozoal diseases in relation to antimicrobial use in feed. Comprehensive data were collected (by questionnaire) enabling AMU assessment by various count-based metrics (i.e., frequency and number of medicated rations), weight-based metrics (i.e., inclusion rate in feed and kilograms consumed), and technical indicators (i.e., milligrams per population correction unit [mg/PCU]). Qualitative information such as reasons for use and frequency of diagnosed diseases provided context to the trends in AMU. Between 2013 and 2017, 646 broiler flocks (14.9 million kg biomass) and 234 turkey flocks (12.4 million kg biomass) were surveyed. Overall, antimicrobials used for the prevention of Clostridium perfringens infections (NE) contributed to 85% (109/128 mg/PCUBr) and 95% (59/62 mg/PCUTk) of the quantity of antimicrobials administered via feed in broiler chickens and turkeys, respectively. Three NE programs were used: either 1, 2, or ≥3 antimicrobials administered throughout the production cycle. The treatment protocol in which a single antimicrobial was used throughout the cycle was the most frequent NE preventive program for broiler chickens (58%) and turkeys (76%). Bacitracin and virginiamycin were the top two most frequently used antimicrobials in both species for NE. For coccidiosis control, ionophores and chemical coccidiostats contributed to 66% (3091 kg) and 68% (1561 kg) of the total feed antimicrobial exposures in broiler chickens and turkeys, respectively. Documented coccidiosis programs included continuous or straight (1 drug/cycle), shuttle or dual control (≥2 drugs/cycle), and vaccination. Variations in coccidiosis programs between species were noted: broiler chickens frequently used a shuttle or dual-control program (68%), whereas turkey flocks used primarily a continuous or straight program (74%). Flocks raised without antibiotics and organic farms (10.3% of broiler chickens and 9.8% turkey flocks) used vaccines to prevent coccidiosis. A small number of broiler flocks (n = 6) used a combination of a vaccination and a coccidiostat during the cycle. During the surveillance timeframe used for this paper, the total feed AMU decreased over time in broiler chickens from 136 to 120 mg/PCUBr and in turkeys from 85 to 62 mg/ PCUTk, with no remarkable changes in the frequency of flocks diagnosed with bacterial and protozoal diseases. Surveillance findings such as these will be used as valid reference points in light of the upcoming changes in Canadian federal AMU regulations and industry-led initiatives aimed at reducing AMU.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.219
Teacher spread0.205 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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