Use of breed‐specific ranges for the vertebral heart scale as an aid to the radiographic diagnosis of cardiac disease in dogs
Bibliographic record
Abstract
The vertebral heart scale was measured on right lateral recumbent thoracic radiographs of 320 dogs of six popular breeds, including for each breed at least 20 dogs with no clinical signs of cardiovascular or respiratory disease and at least 19 dogs with cardiac or respiratory disease. There were significant differences between the mean values of the scale for the different breeds; the normal boxer dogs had a significantly higher mean value than the normal dogs of all the other breeds, and the labrador retrievers had a significantly higher mean value than all the other breeds except the boxer and the cavalier King Charles spaniel. For all the breeds except the boxer, there was a trend for dogs with cardiac disease (but not respiratory disease) to have higher mean values on the scale than normal dogs of the same breed; however, at the optimal value of the scale for distinguishing between dogs of each breed with and without cardiac disease, the sensitivity and specificity were relatively low, in the range 58 to 83 per cent. The scale was most accurate for the diagnosis of cardiac disease in the Yorkshire terrier and the cavalier King Charles spaniel, breeds affected by predominantly dilative forms of cardiac disease. In contrast, it was very inaccurate in the boxer, a breed that has a higher incidence of cardiac diseases associated with concentric hypertrophy.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".