Dietary Management of Labrador Retrievers with Subclinical Hepatic Copper Accumulation
Bibliographic record
Abstract
BACKGROUND: Genetic and environmental factors, including dietary copper intake, contribute to the pathogenesis of copper-associated hepatitis in Labrador retrievers. Clinical disease is preceded by a subclinical phase in which copper accumulates in the liver. OBJECTIVE: To investigate the effect of a low-copper, high-zinc diet on hepatic copper concentration in Labrador retrievers with increased hepatic copper concentrations. ANIMALS: Twenty-eight clinically healthy, client-owned Labrador retrievers with a mean hepatic copper concentration of 919 ± 477 mg/kg dry weight liver (dwl) that were related to dogs previously diagnosed with clinical copper-associated hepatitis. METHODS: Clinical trial in which dogs were fed a diet containing 1.3 ± 0.3 mg copper/Mcal and 64.3 ± 5.9 mg zinc/Mcal. Hepatic copper concentrations were determined in liver biopsy samples approximately every 6 months. Logistic regression was performed to investigate effects of sex, age, initial hepatic copper concentration and pedigree on the ability to normalize hepatic copper concentrations. RESULTS: In responders (15/28 dogs), hepatic copper concentrations decreased from a mean of 710 ± 216 mg/kg dwl copper to 343 ± 70 mg/kg dwl hepatic copper after a median of 7.1 months (range, 5.5-21.4 months). Dogs from a severely affected pedigree were at increased risk for inability to have their hepatic copper concentrations normalized with dietary treatment. CONCLUSIONS AND CLINICAL IMPORTANCE: Feeding a low-copper, high-zinc diet resulted in a decrease in hepatic copper concentrations in a subset of clinically normal Labrador retrievers with previous hepatic copper accumulation. A positive response to diet may be influenced by genetic background. Determination of clinical benefit requires further study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".