[Examination of the density of the brain parenchyma in different dog breeds using quantitative computed tomography].
Bibliographic record
Abstract
In the present study quantitative computed tomography was used to analyze the absorption density in different localisations of the brain parenchyma comparatively in five different dog breeds. The breeds German Shepherd Dog, Dachshund, Boxer, Labrador Retriever and Miniature Poodle were chosen as representatives of different skull shapes and sizes. The mean absorption density for the German Shepherd Dog was 35.8 HU in the cerebellum, 28.5 - 34.8 HU in several locations of the cerebrum, 39.6 HU in the brain stem and 40.8 HU in the hypophyseal region. In the Dachshund, the mean density was 33.8 HU (cerebellum), 34.3 - 44.2 HU (several locations of the cerebrum), 33.3 HU (brain stem) and 38.6 HU (hypophyseal region). The Boxer showed a mean density of 38.7 HU in the cerebellum, 30.2 - 40.8 HU in several locations of the cerebrum, 35.6 HU in the brain stem and 33.1 HU in the hypophyseal region. The mean absorption density in the Labrador Retriever was 37.2 HU in the cerebellum, 29.4 - 32.9 HU (several locations of the cerebrum), 34.7 HU in the brain stem and 47.5 HU in the hypophyseal region. In the Miniature Poodle the mean density was 33.6 HU (cerebellum), 34.9 - 45.5 HU (several locations of the cerebrum), 32.7 HU (brain stem) and 32.4 HU (hypophyseal region). The study showed that the absorption density of the cerebrum, the brain stem and of the hypophyseal region partly were influenced by the different dog breeds. In the cerebellum no difference of density could be seen in the different breeds. The standardised values for the data of absorption densities of the brain parenchyma are the base for further examinations of dogs with clinical CNS diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".