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Record W3136521518 · doi:10.21423/aabppro20054823

Multiattribute Evaluation of Two Simple Tests for the Detection of Cryptosporidium parvum Oocysts in Calf Feces

2005· article· en· W3136521518 on OpenAlexaffabout
Lise A. Trotz‐Williams, S.W. Martin, Donald R. Martin, T.F. Duffield, Kenneth E. Leslie, D.V. Nydam, E. Sockovie, Andrew S. Peregrine

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsMinistry of Health and Long Term CareUniversity of Guelph
Fundersnot available
KeywordsCryptosporidium parvumFecesBiologyCryptosporidiumMicrobiologyVeterinary medicineParasite hostingPathogenPolymerase chain reactionMedicine

Abstract

fetched live from OpenAlex

Cryptosporidium parvum is increasingly recognized as an important pathogen in neonatal dairy calves. As a result, there is a need for simple, inexpensive and quick methods for the detection of C. parvum infection in calf feces. Most diagnostic and screening methods for this parasite that are currently in common use, such as concentration and staining methods and immunofluorescence (Kvac et al, 2003), are expensive and time-consuming, and as such are unsuitable for the screening of large numbers offecal samples in veterinary practice or research. At the Ontario Veterinary College (OVC), a simple sucrose wet mount method without centrifugation has been in use for some time. However, to the authors' knowledge, there are no published reports that have evaluated the performance and utility of this method when used for the detection of C. parvum oocysts in calf feces. In this study, the OVC sucrose wet mount method and a lateral immunochromatography test for detection of C. parvum antigen in feces (BioX Diagnostics, Jem~lle, Belgium) were evaluated using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) with gel electrophoresis as a gold standard.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 head0.026
GPT teacher head0.329
Teacher spread0.303 · 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 designBench or experimental
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

Citations0
Published2005
Admission routes2
Has abstractyes

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