Polymer-Based Materials Built with Additive Manufacturing Methods for Orthopedic Applications: A Review
Bibliographic record
Abstract
Over the last few decades, polymers and their composites have shown a lot of promises in providing more viable alternatives to surgical procedures that require scaffolds and implants. With the advancement in biomaterial technologies, it is possible to overcome the limitations of current methods, including auto-transplantation, xeno-transplantation, and the implantation of artificial mechanical organs used to treat musculoskeletal conditions. The risks associated with these methods include complications, secondary injuries, and limited sources of donors. Three-dimensional (3D) printing technology has the potential to resolve some of these limitations. It can be used for the fabrication of tailored tissue-engineering scaffolds, and implants, repairing tissue defects in situ with cells, or even printing tissues and organs directly. In addition to perfectly matching the patient’s damaged tissue, printed biomaterials can have engineered microstructures and cellular arrangements to promote cell growth and differentiation. As a result, such biomaterials allow the desired tissue repair to be achieved, and could eventually alleviate the shortage of organ donors. As such, this paper provides an overview of different 3D-printed polymers and their composites for orthopedic applications reported in the literature since 2010. For the benefit of the readers, general information regarding the material, the type of manufacturing method, and the biomechanical tests are also reported.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".