Muscular morphometric study of the canine shoulder for the design of 3D-printed endoprostheses in dogs with osteosarcoma of the proximal humerus: a pilot cadaveric study by MRI
Bibliographic record
Abstract
OBJECTIVE: Osteosarcoma frequently affects the proximal humerus in dogs. In veterinary medicine, no therapeutic option for the treatment of osteosarcoma satisfactorily preserves limb function. 3D-printed personalized endoprosthesis offers a promising treatment option. Morphometric data, necessary for the design of the endoprosthesis, are currently lacking in canine patients. Our objective was to acquire the morphometric data necessary to refine the design of the endoprosthesis. ANIMAL: A single canine cadaveric thoracic limb. PROCEDURES: Sagittal proton-density, and sagittal, dorsal, and transverse T1-weighted sequences of the thoracic limb were acquired with a 1.5 Tesla Magnetic Resonance Imaging (MRI) unit. Nineteen muscles of interest were subsequently identified using medical imaging software (Mimics©) and their volume was reconstructed in 3D using computer-aided design (CATIA©). Mormophetric data were recorded for each of the 19 muscles. The same canine cadaver was then dissected to measure the same parameters. RESULTS: All muscles were successfully identified with data consistent with the dissected cadaveric data. Certain muscles were more challenging to isolate on MRI, namely the heads of the triceps brachii, superficial pectoral, and latissimus dorsi. The relative distribution of muscle volumes was similar to historical data. Muscle tissue density was not significantly affected by freezing (1.059 g/cm3). CLINICAL RELEVANCE: MRI is a useful tool to collect morphometric data but imperfect if used alone. This approach was the first attempt to validate more general morphometric data that could be used to refine the design of custom 3D-printed prostheses for limb-sparing of the proximal humerus. Further imaging studies are warranted to refine our model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".