Mechanical Properties of Synthetic Bones Made by Synbone: A Review
Bibliographic record
Abstract
Biomechanical engineers and physicists commonly employ biological bone for biomechanics studies, since they are good representations of living bone. Yet, there are challenges to using biological bone, such as cost, degradation, disease, ethics, shipping, sourcing, storage, variability, etc. Therefore, the Synbone® company has developed a series of synthetic bones that have been used by biomechanical investigators to offset some drawbacks of biological bone. There have been a number of published biomechanical reports using these bone surrogates for dental, injury, orthopedic, and other applications. But, there is no prior review paper that has summarized the mechanical properties of these synthetic bones in order to understand their general performance or how well they represent biological bone. Thus, the goal of this article was to survey the English-language literature on the mechanical properties of these synthetic bones. Studies were included if they quantitatively (a) characterized previously unknown values for synthetic bone, (b) validated synthetic versus biological bone, and/or (c) optimized synthetic bone performance by varying geometric or material parameters. This review of data, pros, cons, and future work will hopefully assist biomechanical engineers and physicists that use these synthetic bones as they develop experimental testing regimes and computational models.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".