Surgical and interventional robotics: Part II
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.026 |
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".