Breed Associations for Canine Exocrine Pancreatic Insufficiency
Bibliographic record
Abstract
BACKGROUND: Knowledge of breed associations is valuable to clinicians and researchers investigating diseases with a genetic basis. HYPOTHESIS: Among symptomatic dogs tested for exocrine pancreatic insufficiency (EPI) by canine trypsin-like immunoreactivity (cTLI) assay, EPI is common in certain breeds and rare in others. Some breeds may be overrepresented or underrepresented in the population of dogs with EPI. Pathogenesis of EPI may be different among breeds. ANIMALS: Client-owned dogs with clinical signs, tested for EPI by radioimmunoassay of serum cTLI, were used. METHODS: In this retrospective study, results of 13,069 cTLI assays were reviewed. RESULTS: An association with EPI was found in Chows, Cavalier King Charles Spaniels (CKCS), Rough-Coated Collies (RCC), and German Shepherd Dogs (GSD) (all P < .001). Chows (median, 16 months) were younger at diagnosis than CKCS (median, 72 months, P < .001), but not significantly different from GSD (median, 36 months, P = .10) or RCC (median, 36 months, P = .16). GSD (P < .001) and RCC (P = .015) were younger at diagnosis than CKCS. Boxers (P < .001), Golden Retrievers (P < .001), Labrador Retrievers (P < .001), Rottweilers (P = .022), and Weimaraners (P = .002) were underrepresented in the population with EPI. CONCLUSIONS AND CLINICAL IMPLICATIONS: An association with EPI in Chows has not previously been reported. In breeds with early-onset EPI, immune-mediated mechanisms are possible or the disease may be congenital. When EPI manifests later, as in CKCS, pathogenesis is likely different (eg, secondary to chronic pancreatitis). Underrepresentation of certain breeds among dogs with EPI has not previously been recognized and may imply the existence of breed-specific mechanisms that protect pancreatic tissue from injury.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".