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Record W2906664918 · doi:10.3168/jds.2017-14345

Evaluation of 3 esterase tests for the diagnosis of subclinical mastitis at dry-off and freshening in dairy cattle

2018· article· en· W2906664918 on OpenAlexaboutno aff
Sahar Kandeel, Ameer Megahed, M.H. Ebeid, Peter D. Constable

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsnot available
Fundersnot available
KeywordsCalifornia mastitis testSomatic cell countMastitisReceiver operating characteristicAnimal scienceMedicineSubclinical infectionCoefficient of variationArea under the curveVeterinary medicineDairy cattleIce calvingBiologyInternal medicineChemistryPathologyChromatographyLactation

Abstract

fetched live from OpenAlex

Subclinical mastitis (SCM) and intramammary infection (IMI) increase esterase activity in the glandular secretions of dairy cattle. Our objective was to evaluate the clinical performance of 3 commercially available esterase tests for diagnosing SCM and IMI. Foremilk samples were collected from 380 quarters (96 cows) at dry-off and from 329 quarters (83 cows) within 4 to 7 d after calving. Quarter somatic cell count (SCC) was measured using the reference method (DeLaval cell counter; De Laval International AB, Tumba, Sweden) with SCM defined as SCC >200,000 cells/mL. Bacterial culture of foremilk samples was used to diagnose IMI based on the growth of ≥100 cfu/mL. The SCC was estimated using 3 PortaSCC tests (PortaCheck, Moorestown, NJ) from the measured esterase activity and the California Mastitis Test (CMT). Clinical performance was evaluated using logistic regression to determine the area under the receiver operating characteristic curve (AUC) and identify test sensitivity (Se) and specificity (Sp) at the optimal cut-point for diagnosing SCM and IMI. Test agreement was also evaluated using the kappa coefficient (κ) and weighted κ. The PortaSCC color test was the best-performing PortaSCC test for diagnosing SCM at dry-off (AUC = 0.90, Se = 0.91, Sp = 0.81, κ = 0.71) and at freshening (AUC = 0.86, Se = 0.74, Sp = 0.95, κ = 0.72), at an optimal cut-point of ≥250,000 cells/mL but required 45 min to produce a result. For comparison, the CMT required 2 min to produce a result and a CMT score of trace or higher was superior to the PortaSCC color test for diagnosing SCM at dry-off (AUC = 0.95, Se = 0.95, Sp = 0.86, κ = 0.81) and freshening (AUC = 0.88, Se = 0.79, Sp = 0.95, κ = 0.76). The PortaSCC quick test was the best-performing PortaSCC test for diagnosing IMI at dry-off (AUC = 0.81, Se = 0.81, Sp = 0.78 κ = 0.40) and required 5 min to produce a result, whereas the PortaSCC color test was the best performing PortaSCC test for diagnosing IMI at freshening (AUC = 0.80, Se = 0.75, Sp = 0.79 κ = 0.38). For comparison, the CMT was inferior to the PortaSCC quick test for diagnosing IMI at dry-off (AUC = 0.73, Se = 0.76, Sp = 0.60, κ = 0.20) but was equivalent to the PortaSCC color test at freshening (AUC = 0.79, Se = 0.58, Sp = 0.93, κ = 0.50). The PortaSCC color and quick tests and CMT were considered good tests for diagnosing SCM and IMI because clinically useful tests typically have an AUC >0.80 and κ >0.6. Based on the test sensitivity, cost, and analysis time, there does not appear to be a persuasive reason to select the PortaSCC tests over the traditional CMT for diagnosing SCM and IMI.

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)0.008
metaresearch head score (Gemma)0.010
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 score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.114
GPT teacher head0.357
Teacher spread0.244 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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