Hematobiochemical profiles and cardiac biomarker assessment in Labrador retriever dogs
Bibliographic record
Abstract
This study aimed to establish baseline hematobiochemical and cardiac biomarker parameters in clinically healthy Labrador Retrievers and assess the influence of body weight on these indices. Sixteen adult Labrador Retrievers (2–6 years, both sexes) were categorized into two groups based on body weight: Group-I (<30 kg) and Group-II (>30 kg). Comprehensive evaluations including hematological, biochemical, and cardiac biomarker analyses were performed following confirmation of the dogs' health status. Hematological parameters including hemoglobin, packed cell volume, RBC, WBC and platelet counts, as well as biochemical parameters such as AST, ALT, total protein, albumin, BUN and creatinine were within normal ranges with no significant intergroup differences. Cardiac biomarker analysis revealed cTnI levels of 0.0056±0.0014 ng/mL in Group-I and 0.0081±0.0018 ng/mL in Group-II, while NT-proBNP levels were 1.9731±0.3085 pmol/L and 3.0789±0.8485 pmol/L, respectively. These differences were not statistically significant indicating stable cardiac health across varying body weights. This study provides valuable baseline data on hematobiochemical and cardiac biomarker parameters in Labrador Retrievers emphasizing the minimal impact of body weight on these indices and contributing to improved cardiac health assessment in this breed.
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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 | 0.000 |
| 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".