3D Printed Anatomy-Specific Fixture for Consistent Glenoid Cavity Position in Shoulder Simulator
Bibliographic record
Abstract
Purpose: Fixation methods for consistent anatomical structure positioning in biomechanical testing can be challenging. Image-based 3D printing is an attractive method for fabrication of biomechanical supports of anatomical structure due to its ability to precisely locate anatomical features with respect to the loading system. Method: A case study is presented to provide a design guide for fixation block fabrication. The anatomy of interest was CT scanned and reconstructed in 3D. The model was imported into commercially available CAD software and modified into a solid object and to create the fixture block. The CAD fixture block is standardized such that anatomical features are always in the same position for the testing system by subtracting the anatomy from a base fixture block. Results: This method allowed a strong immobilization of anatomical specimens and a controlled and consistent positioning feature with respect to the testing system. Furthermore, the fixture block can be easily modified and adapted to anatomical structures of interest using CAD software. Conclusion: This approach allows preservation of the bony anatomy integrity and provides a repeatable and consistent anatomical positioning with respect to the testing system. It can be adapted for other anatomical structures in various other biomechanical settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".