Bibliographic record
Abstract
Image-guided navigation for neurosurgery requires accurate localization of the skull. Localization can be problematic when the patient is in a facedown position. The posterior skull lacks unique identifiable landmarks, which complicates standard localization methods using a tracked pointer. In addition to the lack of anatomical landmarks, trying to access facial surfaces is error-prone when working under the table and problems arise with line-of-sight of the optical tracker. We proposed the use of ultrasound to perform localization and investigated the accuracy of this process. A simulation study was performed to test the feasibility of ultrasound for localization on a plastic skull. An initial localization, using an optically tracked pointer, was performed to partially align pre-operative images and the skull model. Skull surface points were localized by optically tracked ultrasound and used in a surface registration algorithm. Accuracy and reproducibility was then investigated. Evaluation of the proposed localization method found that the average distance of points off the skull surface was 0.6 ± 0.1mm, which meets the same standards set by current commercially available systems for face-up positions. Using tracked ultrasound for registration is feasible for patients in facedown position. We provided a non-invasive method of registration that could be accomplished using one optical tracking camera, and maintains a constant line-of-sight. This project was performed in cooperation with Dr. Gernot Kronreif and the Austrian Center for Medical Innovation and Technology. Dr. Kronreif and his staff are preparing for a clinical test of this localization process.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".