Accuracy of the Surface Contour of Three-Dimensional-Printed Canine Pelvic Replicas
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the differences in surface contour between models of native pelvic bones and their corresponding three-dimensional (3D)-printed replicas. STUDY DESIGN: Digital 3D models of five cadaveric hemipelves and five live dogs with contralateral pelvic fractures were generated based on computed tomographic images and 3D printed. The 3D-printed replicas underwent 3D scanning and digital 3D models of the replicas were created. The digital 3D model of each replica was superimposed onto the model of the native hemipelvis. Errors in the replicas were determined by comparing the distances of 120,000 corresponding surface points between models. The medial surface, lateral surface and dorsal surface of the acetabulum (DSA) of each hemipelvis were selected for further analysis. The root mean square error (RMSE) was compared between various selected areas using a one-way repeated measures analysis of variance, followed by a Bonferroni post-hoc test. RESULTS: < 0.001). CONCLUSION: The 3D-printed replicas were adequate in serving as a template for the pre-contouring of bone plates in fracture repair of pelvic fractures, particularly those that demand accurate reduction such as acetabular fractures.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".