Cardiac biomarker profiles in dogs with naturally occurring precapillary pulmonary hypertension
Bibliographic record
Abstract
INTRODUCTION/OBJECTIVES: This study evaluated circulating amino-terminal pro-B-type natriuretic peptide (NT-proBNP), amino-terminal pro-A-type natriuretic peptide (NT-proANP), and cardiac troponin I (cTnI) concentrations in dogs with precapillary pulmonary hypertension (Pre-PH) and control dogs with respiratory clinical signs but no Pre-PH. ANIMALS: Twenty-six dogs (17 affected, and nine controls) were involved in the study. MATERIALS AND METHODS: This was a sub-study of a large prospective single-center observational study. Dogs underwent blood sample collection, physical examination, and echocardiographic evaluation. Precapillary pulmonary hypertension was diagnosed when a calculated right ventricular-to-right atrial pressure gradient (RV:RA PG) measuring ≥40 mmHg was identified echocardiographically, barring right ventricular outflow obstruction and/or left-sided cardiac disease. RESULTS: Two, nine, and six dogs had mild, moderate, and severe Pre-PH, respectively. Plasma concentrations of NT-proBNP, NT-proANP, and cTnI were significantly higher in the affected group than in the control group (P=0.020, P=0.009, P=0.011, respectively). There was a positive correlation between RV:RA PG and NT-proBNP (r = 0.52), NT-proANP (r = 0.54), and cTnI (r = 0.67) concentrations. DISCUSSION: Precapillary pulmonary hypertension should be included in the differential diagnosis list of elevated cardiac biomarker concentrations in dogs with respiratory signs. STUDY LIMITATIONS: Strict selection criteria reduced group sizes. There were rare missing data points. The diagnosis of Pre-PH was obtained from Doppler echocardiographic RV:RA PG. The disease process causing Pre-PH was not evaluated histopathologically. CONCLUSIONS: Circulating cardiac biomarker concentrations are increased in dogs with Pre-PH and there is a positive correlation between RV:RA PG and NT-proBNP, NT-proANP, and cTnI concentrations.
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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 | 0.000 |
| 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".