Using augmented reality to guide bone conduction device implantation
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
Exact placement of bone conduction implants requires avoidance of critical structures. Existing guidance technologies for intraoperative placement have lacked widespread adoption given accessibility challenges and significant cognitive loading. The purpose of this study is to examine the application of augmented reality (AR) guided surgery on accuracy, duration, and ease on bone conduction implantation. Five surgeons surgically implanted two different types of conduction implants on cadaveric specimens with and without AR projection. Pre- and postoperative computer tomography scans were superimposed to calculate centre-to-centre distances and angular accuracies. Wilcoxon signed-rank testing was used to compare centre-to-centre (C-C) and angular accuracies between the control and experimental arms. Additionally, projection accuracy was derived from the distance between the bony fiducials and the projected fiducials using image guidance coordinates. Both operative time (4.3 ± 1.2 min. vs. 6.6 ± 3.5 min., p = 0.030) and centre-to-centre distances surgery (1.9 ± 1.6 mm vs. 9.0 ± 5.3 mm, p < 0.001) were significantly less in augmented reality guided surgery. The difference in angular accuracy, however, was not significantly different. The overall average distance between the bony fiducial markings and the AR projected fiducials was 1.7 ± 0.6 mm. With direct intraoperative reference, AR-guided surgery enhances bone conduction implant placement while reduces operative time when compared to conventional surgical planning.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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