<title>Image-guided surgery and therapy: current status and future directions</title>
Bibliographic record
Abstract
Image-guided surgery and therapy is assuming an increasingly important role, particularly considering the current emphasis on minimally-invasive surgical procedures. Volumetric CT and MR images have been used now for some time in conjunction with stereotactic frames, to guide many neurosurgical procedures. With the development of systems that permit surgical instruments to be tracked in space, image-guided surgery now includes the use of frame-less procedures, and the application of the technology has spread beyond neurosurgery to include orthopedic applications and therapy of various soft-tissue organs such as the breast, prostate and heart. Since tracking systems allow image- guided surgery to be undertaken without frames, a great deal of effort has been spent on image-to-image and image-to- patient registration techniques, and upon the means of combining real-time intra-operative images with images acquired pre-operatively. As image-guided surgery systems have become increasingly sophisticated, the greatest challenges to their successful adoption in the operating room of the future relate to the interface between the user and the system. To date, little effort has been expended to ensure that the human factors issues relating to the use of such equipment in the operating room have been adequately addressed. Such systems will only be employed routinely in the OR when they are designed to be intuitive, unobtrusive, and provide simple access to the source of the images.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.120 | 0.089 |
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".