Computed tomography of the elbow joint in clinically normal dogs
Bibliographic record
Abstract
OBJECTIVE: To use computed tomography (CT) to provide a detailed description of elbow joint structures in clinically normal dogs. ANIMALS: 6 clinically normal adult mixed-breed dogs weighing 24 to 37 kg and one 12-month-old Labrador Retriever weighing 27 kg. PROCEDURE: To perform CT of both elbow regions, dogs were anesthetized and placed in lateral recumbency. One- and 2-mm contiguous slices were obtained by use of a third generation computed tomographic scanner. Good resolution and anatomic detail were acquired from the computed tomographic images by use of a bone (window width, 3,500 Hounsfield units; window level, 500 Hounsfield units) and soft-tissue setting (window width, 400 Hounsfield units; window level, 66 Hounsfield units). After euthanasia, the forelimbs from the Labrador Retriever were removed and frozen in water at -18 degrees C. Elbow joints were sectioned into approximately 1-mm-thick slab sections by use of an electric planer. Anatomic sections were photographed and compared with the corresponding computed tomographic images. Computed tomographic reconstructions of the elbow joint were created in sagittal and dorsal planes. RESULTS: Structures on the computed tomographic images were matched with structures in the corresponding anatomic sections. The entire humeroradioulnar joint surface could be evaluated on the reconstructed images in the sagittal and dorsal plane. CONCLUSIONS AND CLINICAL RELEVANCE: Computed tomographic images provide full anatomic detail of the bony structures of the elbow joint in dogs. Muscles, large blood vessels, and nerves can also be evaluated. These results could be used as a basis for evaluation of computed tomographic images of the forelimbs of dogs with elbow joint injuries.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".