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Record W4392303056 · doi:10.3168/jds.2023-24329

Associations of the Swiss national reporting system's antimicrobial use data and management practices in dairy cows on tiestall farms

2024· article· en· W4392303056 on OpenAlexaboutno aff
Belinda Köchle, Véronique Bernier Gosselin, Guy-Alain Schnidrig, Jens Becker

Bibliographic record

VenueJournal of Dairy Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsnot available
FundersFondation Sur-la-CroixVontobel-Stiftung
KeywordsAntimicrobialBusinessDairy cattleAgricultural scienceBiotechnologyEnvironmental healthMedicineAnimal scienceBiologyMicrobiology

Abstract

fetched live from OpenAlex

Antimicrobial use (AMU) in Switzerland is above target and requires reduction, especially in dairy cattle. Measuring AMU is pivotal to identifying starting points for AMU reduction, and so are studies investigating its potential drivers in dairy farms worldwide. However, although AMU in dairy farms is high, studies estimating AMU specifically in tiestall farms are scarce. Tiestalls are a common housing system and their prevalence among dairy farms accounts to approximatively 73%, 41%, and 40% in Canada, the United States, and Switzerland, respectively. The objectives of this cross-sectional, retrospective observational study were to estimate AMU using the newly established Swiss national reporting system for AMU in livestock and to identify associated factors on Swiss tiestall dairy farms. We calculated the treatment incidence (TI) by using the European Medicines Agency methodology and their defined daily dose (DDD) and defined course dose (DCD) standards. Data on factors potentially associated with AMU were obtained through personal interviews with farm managers on 221 farms. Retrospectively, during a 1-year period, data on a total of 7,619 treatments were extracted from the national database. Associations between management factors and TI were analyzed using a generalized linear model with gamma distribution. The mean (± SD) overall TI was 5.46 DDD/cow-year (± 4.10 DDD/cow-year). Intramammary treatment during lactation accounted for highest TI (3.24 ± 3.16 DDD/cow-year), whereas dry-cow therapy accounted for lowest TI (0.44 ± 0.49 DCD/cow-year). We found that 5 of the investigated management factors were significantly associated with TI. Organic production (estimate = -2.16; 95% CI = -3.62 to -0.70) and herd size (estimate = -0.81; 95% CI = -1.23 to -0.39) were negatively associated with TI. Specific cow breeds (Brown Swiss estimate = 1.56; 95% CI = 0.45-2.68) and Holstein Friesian (estimate = 1.42; 95% CI = 0.03-2.82; reference: other breeds) and the use of hygienic powders on the lying area (estimate = 1.10; 95% CI = 0.04-2.17) were positively associated with TI. Overall, the Swiss national reporting system is a valuable tool for AMU estimation. Several herd characteristics and management factors were associated with AMU in tiestall farms. Further studies focusing on factors associated with AMU and which are amenable to intervention will help improve stewardship programs and subsequently reduce AMU in dairy cows.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.150
GPT teacher head0.347
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2024
Admission routes1
Has abstractyes

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