Copper toxicosis in the Bedlington terrier: a diagnostic dilemma
Bibliographic record
Abstract
Diagnosis of copper toxicosis (CT) in Bedlington terriers by the quantitative and qualitative assessment of copper (Cu) in, and pathology of, biopsies has been largely superseded by a DNA-based assay which uses a microsatellite marker (C04107) linked to the CT disease allele. A retrospective study was conducted comprising 154 liver biopsies from Bedlington terriers with 22 matched DNA markers to compare the two methods in the diagnosis of CT. For the biopsy method, three categories (phenotypes) were identified based on analytical and morphological criteria: 'unaffected' in 83 samples (54 per cent), where Cu was much less than 400 microg/g, and there was an absence of visual Cu or liver damage; 'intermediate' in 18 samples (12 per cent), where Cu was less than 400 microg/g, and there was limited histochemical Cu and no/equivocal damage; and 'affected' in 53 samples (34 per cent), where Cu was greater than 400 microg/g, there was histochemical Cu and liver damage was poorly related to Cu content. In the DNA assay, which was used alone on unrelated individuals, the microsatellite marker failed to identify the CT status of any of the groups. Liver biopsy remains a reliable indicator of Cu accumulation and progressive liver disease in individual dogs. The microsatellite marker C04107 has a predictive value only when supported by a pedigree.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".