Application of fused deposition modeling (FDM) on bone scaffold manufacturing process: A review
Bibliographic record
Abstract
Some of the health issues that are becoming more prevalent each year include bone disease and fractures. Because the natural healing process of bones takes a long time, a bone grafting procedure is required so that the patient's condition can improve rapidly. Because bone grafting procedures such as autographs, allographs, and xenografts have limits, bone replacement is constructed by employing biomaterials in the form of a bone scaffold via additive manufacturing. As a result, fused deposition modeling (FDM) is a proposed technology for the manufacturing process because it is straightforward, capable of producing complex parts and adjustable shapes, and has minimal operational expenses. However, implementing this technique is challenging because of the scarcity of biocompatible and bioactive materials that are suited. This technology has a number of limitations, including a limited variety of biocompatible and bioactive materials, the most appropriate microarchitecture of bone scaffold, and the establishment of printing parameters that can produce bone scaffold with the strongest mechanical properties. This article discusses current advancements in the use of FDM technologies for bone scaffold production.
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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 | 0.001 |
| 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) | 0.003 | 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".