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Association of Dietary Copper and Zinc Levels with Hepatic Copper and Zinc Concentration in Labrador Retrievers

2012· article· en· W2152548309 on OpenAlexaboutno aff
Hille Fieten, B.D. Hooijer‐Nouwens, Vincent Biourge, Peter A. J. Leegwater, Adrian Watson, T.S.G.A.M. van den Ingh, Jan Jaap Rothuizen

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2012
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsnot available
FundersMars PetcareRoyal CaninUniversiteit Utrecht
KeywordsMedicineZincCopperInternal medicineGastroenterologyMetallurgy

Abstract

fetched live from OpenAlex

BACKGROUND: Copper-associated hepatitis is an inherited disease in the Labrador Retriever. Apart from genetic factors, dietary intake of copper and zinc are suspected to play a role in the pathogenesis. OBJECTIVES: To investigate whether dietary copper and zinc levels of commercially available dry diets are associated with hepatic copper and zinc concentrations in Labrador Retrievers. ANIMALS: Fifty-five Labrador Retrievers that were fed a single brand and type of commercial dry food for at least 1 year. Of these, 44 dogs were family members of Labrador Retrievers with copper-associated hepatitis. METHODS: Liver biopsies, blood samples, and diet samples were obtained. Liver specimens were scored histologically and copper and zinc concentrations were quantified. Dietary concentrations of copper and zinc were measured. The association between dietary intake of copper and zinc and hepatic copper and zinc concentrations was investigated by linear regression analysis. RESULTS: High dietary copper and low dietary zinc levels were significantly associated with high hepatic copper levels. No association between dietary intake and hepatic zinc was present. CONCLUSIONS AND CLINICAL RELEVANCE: Dietary copper and zinc at current levels in commercially available dry dog food can influence hepatic copper and can be a risk factor for the development of copper-associated hepatitis in Labrador Retrievers with a genetic susceptibility to copper.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.041
GPT teacher head0.338
Teacher spread0.297 · 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

Citations55
Published2012
Admission routes1
Has abstractyes

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