Comparative characteristics of the bones forming the elbow joint in dogs of service breeds according to computed tomography
Bibliographic record
Abstract
Service dogs in the Russian Federation play the most important role in the executive authorities, performing complex tasks in which these trained animals are indispensable to this day. The most common service dog breeds on the territory of the Russian Federation participating in search, patrol, search, special, escort and guard activities are the German Shepherd, Belgian Shepherd, Spaniel, Labrador. Dysplasia of the elbow joint and injury with high-energy injuries, often has only surgical methods of treatment in dogs, it is necessary to develop the safest, most effective surgical approaches to the elbow joint with anatomical and topographic justification. The study was conducted in the network of veterinary centers "Kotonai" in St. Petersburg. The object of the study was dogs of the Labrador breed in the amount of 10 individuals. Research methods: morphometry, computed tomography. As a result of the study, it was found that the humerus has the lowest growth potential in the first age group, however, it has the largest area of epiphyseal zones, which may affect the planning of surgical interventions in the area of the elbow joint, as well as in the treatment of epiphysiolysis of the humerus. Evaluation of ossification of the processes of the ulna by computed tomography in case of suspected dysplastic changes in the type of disease of the medial coronary process and non-fusion of the hooked process is possible at the age of 5.5 months in Labrador dogs. To obtain more accurate data on the comparative anatomy of the bones forming the elbow joint, the study of this topic should be continued, and to reduce measurement errors, studies should be conducted on groups according to the sex of the animal.
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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 | 0.002 | 0.001 |
| 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".