Three-dimensional Printing Technology in Orthopaedics
Bibliographic record
Abstract
Three-dimensional (3-D) printing technology is affecting how orthopaedic surgeries are planned and executed. Like many innovations, 3-D printers are becoming smaller, more affordable, and more accessible. Free access to open-source 3-D imaging software has also made clinical implementation of this technology widely feasible. Within the last decade, 3-D printing advancements have improved the way orthopaedic surgeons can approach both common and complex cases. Advanced imaging studies can be used to create musculoskeletal models, which can then be used to create custom orthopaedic guides and instruments. Similarly, 3-D printing is being applied to improve the field of biologic therapies in orthopaedics. Application of 3-D printing technology has been associated with important improvements in education, preoperative planning, surgical care, and patient-specific devices and treatments. Improvements in cost-effectiveness, access, and usability of 3-D printing technology have made it possible for orthopaedic surgeons to use this powerful tool using desktop 3-D printers in their clinic or office. The types of printers and materials available to print are constantly expanding, but many of the basic 3-D printing principles persist throughout these advances in the field. A clear understanding of this technology is important to the clinical implementation of 3-D printing for current and future practice of orthopaedic care.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".