Breed Differences in Natriuretic Peptides in Healthy Dogs
Bibliographic record
Abstract
BACKGROUND: Measurement of plasma concentration of natriuretic peptides (NPs) is suggested to be of value in diagnosis of cardiac disease in dogs, but many factors other than cardiac status may influence their concentrations. Dog breed potentially is 1 such factor. OBJECTIVE: To investigate breed variation in plasma concentrations of pro-atrial natriuretic peptide 31-67 (proANP 31-67) and N-terminal B-type natriuretic peptide (NT-proBNP) in healthy dogs. ANIMALS: 535 healthy, privately owned dogs of 9 breeds were examined at 5 centers as part of the European Union (EU) LUPA project. METHODS: Absence of cardiovascular disease or other clinically relevant organ-related or systemic disease was ensured by thorough clinical investigation. Plasma concentrations of proANP 31-67 and NT-proBNP were measured by commercially available ELISA assays. RESULTS: Overall significant breed differences were found in proANP 31-67 (P < .0001) and NT-proBNP (P < .0001) concentrations. Pair-wise comparisons between breeds differed in approximately 50% of comparisons for proANP 31-67 as well as NT-proBNP concentrations, both when including all centers and within each center. Interquartile range was large for many breeds, especially for NT-proBNP. Among included breeds, Labrador Retrievers and Newfoundlands had highest median NT-proBNP concentrations with concentrations 3 times as high as those of Dachshunds. German Shepherds and Cavalier King Charles Spaniels had the highest median proANP 31-67 concentrations, twice the median concentration in Doberman Pinschers. CONCLUSIONS AND CLINICAL IMPORTANCE: Considerable interbreed variation in plasma NP concentrations was found in healthy dogs. Intrabreed variation was large in several breeds, especially for NT-proBNP. Additional studies are needed to establish breed-specific reference ranges.
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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 | 0.000 |
| 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) | 0.001 | 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".