Muscular Morphometric Study of the Canine Shoulder with In Silico Functional Modelling of Personalized 3D-Printed Endoprostheses in Dogs with Osteosarcoma of the Proximal Humerus: A Pilot Cadaveric Study by MRI
Bibliographic record
Abstract
Introduction: Osteosarcoma frequently affects the canine proximal humerus. In veterinary medicine no satisfactory therapeutic option is available to preserve the function of the limb. 3D-printed personalized prostheses would be a valuable therapeutic option. Morphometric data are needed to design the prostheses. They are currently lacking in canine patients. Our objective was to acquire morphometric data needed to refine the design of the prostheses. Materials and Methods: MRI was performed on one canine cadaveric thoracic limb. Muscles of interest were identified using a medical images software (Mimics). Morphometric data were recorded for each of the 19 muscles. The same canine cadaver was then dissected to measure the same variables. Those data were used in silico to model the relative importance of each muscle during motion. Results: All muscles were successfully identified with data consistent with the dissected cadaveric data. Some muscles were more challenging to isolate on the MRI, namely the heads of the triceps , superficial pectoral, and latissimus dorsi . Relative repartition of muscle volume was similar to historical data. The first modelling with a cuff-rotatory model prosthesis showed the importance for the shoulder stability of the superficial pectoral (transversus), latissimus dorsi , infraspinatus, and subscapularis . Discussion/Conclusion: MRI is a useful tool to collect morphometric data but imperfect if used solely. This approach was a first attempt to validate more general morphometric data that could be used to refine the design of 3D-printed personalized prostheses for limb-sparing of the proximal humerus. Further imaging studies are warranted to refine our model. Acknowledgement: This project was funded by ÉTS Laboratory on Shape Memory and Intelligent system (B.V.), Canada Research Chair in Engineering Innovations in Spinal Trauma (P.Y.) and University of Montreal internal funds (B.L.). Publication History Article published online: 26 October 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".