Point-of-Care Orthopedic Oncology Device Development
Bibliographic record
Abstract
BACKGROUND: The triad of 3D design, 3D printing, and xReality technologies is explored and exploited to collaboratively realize patient-specific products in a timely manner with an emphasis on designs with meta-(bio)materials. METHODS: A case study on pelvic reconstruction after oncological resection (osteosarcoma) was selected and conducted to evaluate the applicability and performance of an inter-epistemic workflow and the feasibility and potential of 3D technologies for modeling, optimizing, and materializing individualized orthopedic devices at the point of care (PoC). RESULTS: Image-based diagnosis and treatment at the PoC can be readily deployed to develop orthopedic devices for pre-operative planning, training, intra-operative navigation, and bone substitution. CONCLUSIONS: Inter-epistemic symbiosis between orthopedic surgeons and (bio)mechanical engineers at the PoC, fostered by appropriate quality management systems and end-to-end workflows under suitable scientifically amalgamated synergies, could maximize the potential benefits. However, increased awareness is recommended to explore and exploit the full potential of 3D technologies at the PoC to deliver medical devices with greater customization, innovation in design, cost-effectiveness, and high quality.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".