Comparison of cadaveric and isomorphic virtual haptic simulation in temporal bone training
Bibliographic record
Abstract
Background Virtual surgery may improve learning and provides an opportunity for pre-operative surgical rehearsal. We describe a novel haptic temporal bone simulator specifically developed for multicore processing and improved visual realism. A position locking algorithm for enhanced drill-bone interaction and haptic fidelity is further employed. The simulation construct is evaluated against cadaveric education. Methods A voxel-based simulator was designed for multicore architecture employing Marching Cubes and Laplacian smoothing to perform real-time haptic and graphic rendering of virtual bone. Ten Otolaryngology trainees dissected a cadaveric temporal bone (CTB) followed by a virtual isomorphic haptic model (VM) based on derivative microCT data. Participants rated 1) physical characteristics, 2) specific anatomic constructs, 3) usefulness in skill development and 4) perceived educational value. The survey instrument employed a Likert scale (1-7). Results Residents were equivocal about the physical properties of the VM, as cortical (3.2 ± 2.0) and trabecular (2.8 ± 1.6) bone drilling character was appraised as dissimilar to CTB. Overall similarity to cadaveric training was moderate (3.5 ± 1.8). Residents generally felt the VM was beneficial in skill development, rating it highest for translabyrinthine skull-base approaches (5.2 ± 1.3). The VM was considered an effective (5.4 ± 1.5) and accurate (5.7 ± 1.4) training tool which should be integrated into resident education (5.5 ± 1.4). The VM was thought to improve performance (5.3 ± 1.8) and confidence (5.3 ± 1.9) and was highly rated for anatomic learning (6.1 ± 1.9). Conclusion Study participants found the VM to be a beneficial and effective platform for learning temporal bone anatomy and surgical techniques. They identify some concern with limited physical realism likely owing to the haptic device interface. This study is the first to compare isomorphic simulation in education. This significantly removes possible confounding features as the haptic simulation was based on derivative imaging.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".