Personalized implant for high tibial opening wedge: Combination of solid freeform fabrication with combustion synthesis process
Bibliographic record
Abstract
In this work a new generation of bioceramic personalized implants were developed. This technique combines the processes of solid freeform fabrication (SFF) and combustion synthesis (CS) to create personalized bioceramic implants with tricalcium phosphate (TCP) and hydroxyapatite (HA). These porous bioceramics will be used to fill the tibial bone gap created by the opening wedge high tibial osteotomy (OWHTO). A freeform fabrication with three-dimensional printing (3DP) technique was used to fabricate a metallic mold with the same shape required to fill the gap in the opening wedge osteotomy. The mold was subsequently used in a CS process to fabricate the personalized ceramic implants with TCP and HA compositions. The mold geometry was designed on commercial 3D CAD software. The final personalized bioceramic implant was produced using a CS process. This technique was chosen because it exploits the exothermic reaction between P₂O₅ and CaO. Also, chemical composition and distribution of pores in the implant could be controlled. To determine the chemical composition, the microstructure, and the mechanical properties of the implant, cylindrical shapes were also fabricated using different fabrication parameters. Chemical composition was performed by X-ray diffraction. Pore size and pore interconnectivity was measured and analyzed using an electronic microscope system. Mechanical properties were determined by a mechanical testing system. The porous TCP and HA obtained have an open porous structure with an average 400 µm channel size. The mechanical behavior shows great stiffness and higher load to failure for both ceramics. Finally, this personalized ceramic implant facilitated the regeneration of new bone in the gap created by OWHTO and provides additional strength to allow accelerated rehabilitation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".