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Performance of a milk leukocyte differential test for decision-making in a selective dry cow therapy program

2019· article· en· W3092814138 on OpenAlexaff
J. Denis-Robichaud, R.A. Almeida, S.J. Ivey, Rudy Rodriguez, Martha E. Payne, K.E. Leslie, M. E. Hockett

Bibliographic record

VenueThe Bovine Practitioner · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsService de Recherche et d'EXpertise en Transformation des Produits Forestiers
Fundersnot available
KeywordsUdderMedicineHerdGold standard (test)MastitisLactationIncidence (geometry)Veterinary medicineAnimal scienceInternal medicinePregnancyBiologyMathematicsPathology

Abstract

fetched live from OpenAlex

The objectives of this study were 1) to determine the operating characteristics of a commercial milk leukocyte differential (MLD) test to detect intramammary infections in quarters of late-lactation dairy cows as compared to bacteriological culture and 2) to evaluate the milk production and udder health parameters between cows treated following blanket vs selective dry cow therapy (DCT) using the MLD test results. In a first experiment, the MLD test was compared to the bacteriological culture results (gold standard) of 363 quarters from 94 cows. The sensitivity, specificity, and predictive values for the identification of infection using the MLD test were determined. Sensitivity ranged from 44% to 77%, and specificity from 54% to 92%. In the second experiment blanket DCT was compared to selective DCT based on the results of MLD test, and treating only positive quarters; a total of 328 cows were randomly assigned to 1 of the 2 treatment groups. The proportion of quarters positive to bacteriological culture, and the incidence rate of moderate and severe cases of clinical mastitis events, did not differ between treatment groups. Results of these experiments provide information to support decision-making in a selective DCT program in low-SCC herds using the MLD test.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.564

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)0.0010.000

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 head0.021
GPT teacher head0.279
Teacher spread0.258 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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