Bibliographic record
Abstract
Abstract Computer‐aided design (CAD) plays a key role in a variety of medical applications including prosthesis design, surgical implant design, blood flow analysis, preoperative planning for surgical operations, and computer‐assisted surgery. CAD tools and technology developed for mechanical design have been successfully applied to geometric modeling, visualizing, animating, and analyzing the natural functional behavior of anatomical structures including human skeletal and vascular systems. These shape modeling and visualization tools provide a significant amount of design information concerning object shape, dimensional parameters, component materials, material flow, and interference checking. New developments in virtual reality (VR) and rapid prototyping (RP) technologies have also enabled biomedical engineers to create detailed three‐dimensional (3‐D) models of anatomical structures directly from the CAD database. The immersive VR environments provide designers, physicians, and surgeons with the ability to interactively manipulate the geometric CAD models with 3‐D displays and haptic devices. In contrast, the fabricated RP prototypes give surgeons a realistic hard copy of complex structures before a medical implant is inserted or a surgical procedure is performed. The shift from the purely graphical interpretation of complex geometric models displayed on a computer monitor to an interactive visual‐tactile representation of the anatomical structure has the ability to deliver a new level of spatial understanding to designers and medical personnel.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.152 | 0.067 |
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".