Bibliographic record
Abstract
The main advantages of robot-assisted orthopedic surgery over conventional orthopedic techniques are improved accuracy and precision in the preparation of bone surfaces, more reliable and reproducible outcomes, and greater spatial accuracy. Orthopedic surgery is ideally suited for the application of robotic systems. The ability to isolate and rigidly fix bones in known positions allows robotic devices to be securely fixed to the bone. As such, the bone is treated as a fixed object, simplifying the computer control of the robotic system. Commercially available robotic systems can be categorized as either passive or active devices, or can be categorized as positioning or milling/cutting devices. Computer assisted orthopedic surgery is a related area of technological development in orthopedics; however, robot-assisted orthopedic surgery can achieve levels of accuracy, precision, and safety not capable with computer assisted orthopedic surgery. Applications of robot-assisted orthopedic surgery currently under investigation include total hip and knee replacement, tunnel placement for reconstruction of knee ligaments, and trauma and spinal procedures. Several short-term studies demonstrate the feasibility of robotic applications in orthopedics, however, there are no published long-term data defining the efficacy of robot-assisted orthopedic surgery. Issues of cost, training, and safety must be addressed before robot-assisted orthopedic surgery becomes widely available. Robot-assisted orthopedic surgery is still very much in its infancy but it has the potential to transform the way orthopedic procedures are done in the future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".