Bibliographic record
Abstract
Since the discovery of X-rays, medical imaging has played a major role in the guidance of surgical procedures. While medical imaging began with simple X-ray plates to indicate the presence of foreign objects within the human body, the advent of the computer has been a major factor in the recent development of this field. Imaging techniques have grown greatly in their sophistication and can now provide the surgeon with high quality three-dimensional images depicting not only the normal anatomy and pathology, but also vascularity and function. One key factor in the advances in Image-Guided Surgery (IGS) is the ability not only to register images derived from the various imaging modalities amongst themselves, but also to register them to the patient. The other crucial aspect of IGS is the ability to track instruments in real time during the procedure, and to portray them as part of a realistic model of the operative volume. Stereoscopic and virtual-reality techniques can usefully enhance the visualization process. IGS nevertheless relies heavily on the assumption that the images acquired prior to surgery, and upon which the surgical guidance is based, accurately represent the morphology of the tissue during the surgical procedure. In many instances this assumption is invalid, and intra-operative real-time imaging, using interventional MRI, Ultrasound, and electrophysiological recordings are often employed to overcome this limitation. Although now in extensive clinical use, IGS is often currently perceived as an intrusion into the operating room. It must evolve towards becoming a routine surgical tool, but this will only happen if natural and intuitive human interfaces are developed for these systems.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".