Serum C‐reactive protein concentrations in healthy Miniature Schnauzer dogs
Bibliographic record
Abstract
BACKGROUND: C-reactive protein (CRP) is a sensitive marker for inflammation in people and dogs. In people, an association between CRP concentration and atherosclerosis has been reported. Atherosclerosis is rare in dogs, but the Miniature Schnauzer breed may be at increased risk for developing this vascular disease. It is not known if CRP concentrations in Miniature Schnauzer dogs differ from those in other dog breeds. OBJECTIVES: Our objectives were to validate an automated human CRP assay for measuring CRP in dogs and compare CRP concentrations in healthy Miniature Schnauzer dogs with those in non-Miniature Schnauzer breeds. METHODS: Sera from 37 non-Miniature Schnauzer dogs with inflammatory disease were pooled and used to validate a human CRP immunoturbidimetric assay for measuring canine CRP. Blood was collected from 20 healthy Miniature Schnauzer dogs and 41 healthy dogs of other breeds. Median serum CRP concentration of healthy Miniature Schnauzer dogs was compared with that of healthy non-Miniature Schnauzer dogs. RESULTS: The human CRP assay measured CRP reliably with linearity between 0 and 20 mg/L. CRP concentration for healthy Miniature Schnauzer dogs (median 4.0 mg/L, minimum-maximum 0-18.2 mg/L) was significantly higher than for the healthy non-Miniature Schnauzer dogs (median 0.1 mg/L, minimum-maximum 0-10.7 mg/L); 17 of the 20 Miniature Schnauzer dogs had values that overlapped with those of the non-Miniature Schnauzer dogs. CONCLUSIONS: Median CRP concentration of Miniature Schnauzer dogs was slightly higher than that of other breeds of dogs. A relationship between higher CRP concentration in Miniature Schnauzer dogs and idiopathic hyperlipidemia, pancreatitis, and possible increased risk for atherosclerosis remains to be determined.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".