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Record W2995977133 · doi:10.3168/jds.2018-16217

Noninferiority study evaluating the efficacy of a teat disinfectant containing copper and zinc for prevention of naturally occurring intramammary infections in an automatic milking system

2019· article· en· W2995977133 on OpenAlexaff
Claudina Vissio, Armin Mella, Luis Amestica, Martín Pol

Bibliographic record

VenueJournal of Dairy Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsMilkingDisinfectantAutomatic milkingMastitisVeterinary medicineMedicineAnimal scienceHygieneBiologyMicrobiologyLactationIce calving

Abstract

fetched live from OpenAlex

The aim of this study was to demonstrate the noninferiority of a novel teat disinfectant based on copper and zinc (ZkinCu; Copper Andino, Santiago de Chile, Chile) compared with a previously proven glycolic acid active disinfectant (OceanBlu; DeLaval, Kansas City, MO) as a positive control, with respect to the incidence of new intramammary infections under natural challenge conditions on a commercial robotic dairy farm. This study was conducted in 6 robotic pens of approximately 60 milking cows each. The pens were randomly assigned to 1 of the 2 studied disinfectants. Throughout the 8 wk study, the same pre- and post-milking teat disinfectant was used in each pen. The same milking procedures were used in each robot throughout the study. Pre-milking hygiene consisted of applying the disinfectant (OceanBlu or ZkinCu) with the robotic arm. The same product was applied on the teats after milking. At the beginning of the study, all quarters of all study cows were sampled. In successive samplings (wk 2, 4, 6, and 8), composite milk samples were collected on farm to determine SCC. Once composite SCC results were available (2 d) and based on an SCC of ≥100,000 cells/mL, quarter milk samples underwent bacteriological culture. Clinical mastitis was identified by study personnel. Intramammary infection in biweekly quarter milk samples was determined based on composite SCC levels (≥100,000 cells/mL) and the presence of bacteria. A new IMI was defined as a quarter in which the organism isolated was not present in the previous bacteriological sample, or the previous composite SCC sample was <100,000 cells/mL. Clinical mastitis samples were also considered to be new IMI. The trial was designed as a positive control field trial, in which the objective was to show noninferiority of ZkinCu versus the control (OceanBlu). The overall crude incidences of new IMI for 2 wk at risk were 4.9 and 7.3% for the ZkinCu and OceanBlu groups, respectively. The predominant organisms recovered from quarters with new IMI were Streptococcus uberis, Corynebacterium spp., and coagulase-negative staphylococci in both the ZkinCu and OceanBlu groups. The risk of infection in the OceanBlu group was higher (β = 0.644; 95% confidence interval = 0.05-1.22). The interaction of treatment by week was not significant. The new IMI rate estimates (95% confidence interval) for ZkinCu and OceanBlu were 1.7% (0.8-2.5) and 3.2% (1.7-4.7), respectively. One novel aspect of this study is that it was one of the first commercial noninferiority trials to evaluate a new pre- and post-milking teat disinfectant in a dairy herd with an automatic milking system. The experimental teat disinfectant ZkinCu, evaluated in this field trial with naturally occurring IMI, showed noninferiority relative to the positive control for the prevention of new IMI. This study was conducted in a herd with an automatic milking system, and the results are applicable to herds with similar characteristics. Additional studies are needed to ensure reproducibility under different management conditions.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.341
Teacher spread0.300 · 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 designNon-randomized trial
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

Citations13
Published2019
Admission routes1
Has abstractyes

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