Demographic and histopathologic features of dogs with abnormally high concentrations of hepatic copper
Bibliographic record
Abstract
BACKGROUND: Copper associated hepatopathy (CAH) has become an important and prevalent disease since the 1990's, coincidental with changes in copper (Cu) content in commercial dog foods. Knowing the demographic and histopathologic features related to hepatic Cu concentrations might aid in diagnosing CAH in dogs. HYPOTHESIS/OBJECTIVES: The primary aim was to identify demographic and histopathologic features associated with abnormally high hepatic Cu concentrations. ANIMALS: Dogs that underwent liver histopathology and Cu quantification at a veterinary diagnostic laboratory between July 2010 and February 2020. METHODS: Data was retrospectively collected from an electronic database. A Gaussian multiple regression model on the log scale was used to evaluate associations between hepatic Cu and a set of demographic and histologic features selected with machine learning methods. RESULTS: Of 4559 cases meeting criteria, 50% had hepatic Cu > 400 and 19% had Cu > 1000 ppm (parts per million) dry weight (reference range 120-400). Median hepatic Cu was 391 ppm, range 4.5 to 31500. Age was negatively associated (P < .02), but specific breeds (Doberman pinscher, Labrador retriever, and West Highland white terrier) were positively associated with abnormally high hepatic Cu (P < .001). Severity of inflammation (mild, moderate, and severe) and necrosis/apoptosis were associated with abnormally high hepatic Cu (P < .01). CONCLUSION AND CLINICAL IMPORTANCE: Abnormally high hepatic Cu is prevalent in hepatic biopsies from dogs. Machine learning modeling showed that necroinflammation, not cholestasis or cirrhosis, on hepatic histopathology, is predictive of higher hepatic Cu and might be a reliable histologic predictor of CAH.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".