Surgical and interventional robotics: Part II
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A large family of medical interventions can be represented by a model that is analogous to industrial manufacturing systems. If the right information is available, they can be planned ahead of time and executed in a reasonably predictable manner. We, therefore, have classified them as surgical computer-aided design (CAD)-computer-aided manufacturing (CAM) systems, having three key concepts: 1) surgical CAD, in which medical images, anatomical atlases, and other information are combined preoperatively to model an individual patient; the computer then assists the surgeon in planning and optimizing an appropriate intervention 2) surgical CAM, in which real-time medical images and other sensor data are used to register the preoperative plan to the actual patient and the model and the plan are updated throughout the procedure; the physician performs the actual surgical procedure with the assistance of the computer, using appropriate technology (robotics, mechatronics, optical guidance, perceptual guidance, etc.) for the intervention 3) surgical total quality management (TQM), which reflects the important role that the computer can play in reducing surgical errors and in promoting more consistent and improved execution of procedures. Successful procedures are also included in procedural statistical atlases and fed back into the system for pre- and intraoperative planning. This article, primarily concerned with robotics and mechatronics, concentrates on the surgical action (surgical CAM), although for the sake of completeness, major issues in surgical planning (surgical CAD) and postoperative data analysis (surgical TQM) are also included. This article is the second installment of a three-part series on surgical and interventional robotics.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 it