Biomechanical properties of artificial bones made by Sawbones: A review
Bibliographic record
Abstract
Biomedical engineers and physicists frequently use human or animal bone for orthopaedic biomechanics research because they are excellent approximations of living bone. But, there are drawbacks to biological bone, like degradation over time, ethical concerns, high financial costs, inter-specimen variability, storage requirements, supplier sourcing, transportation rules, etc. Consequently, since the late 1980s, the Sawbones® company has been one of the world's largest suppliers of artificial bones for biomechanical testing that counteract many disadvantages of biological bone. There have been many published reports using these bone analogs for research on joint replacement, bone fracture fixation, spine surgery, etc. But, there exists no prior review paper on these artificial bones that gives a comprehensive and in-depth look at the numerical data of interest to biomedical engineers and physicists. Thus, this paper critically reviews 25 years of English-language studies on the biomechanical properties of these artificial bones that (a) characterized unknown or unreported values, (b) validated them against biological bone, and/or (c) optimized different design parameters. This survey of data, advantages, disadvantages, and knowledge gaps will hopefully be useful to biomedical engineers and physicists in developing mechanical testing protocols and computational finite element models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.002 |
| 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".