Relationships between heart rate and age, bodyweight and breed in 10,849 dogs
Bibliographic record
Abstract
OBJECTIVES: To evaluate relationships between heart rate and clinical variables in healthy dogs and dogs examined at a referral hospital. METHODS: Clinical data were extracted from the electronic patient records of a first opinion group (5000 healthy dogs) and a referral hospital (5849 dogs). Univariable and multi-variable general linear models were used to assess associations between heart rate and clinical characteristics. Separate multi-variable models were constructed for first opinion and referral populations. RESULTS: In healthy dogs, heart rate was negatively associated with bodyweight (P<0.001) but was higher in Chihuahuas. The mean difference in heart rate between a 5 and 55 kg dog was 10.5 beats per minute. In dogs presenting to a referral hospital, heart rate was negatively associated with bodyweight (P<0.001) and the following breeds; border collie, golden retriever, Labrador retriever, springer spaniel and West Highland white terrier and positively associated with age, admitting service (emergency and critical care, emergency first opinion and cardiology) and the following breeds; Cavalier King Charles spaniel, Staffordshire bull terrier and Yorkshire terrier. CLINICAL SIGNIFICANCE: Bodyweight, age, breed and disease status all influence heart rate in dogs, although these factors account for a relatively small proportion of the overall variability in heart rate.
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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 | 0.000 |
| 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".