Elements of virtual temporal bone surgery: Manipulandum format may be more important to surgeons than haptic device force capabilities
Bibliographic record
Abstract
Background Temporal bone simulations are critiqued for poor drill‐bone interaction. This project appraises the import of increasing haptic device and manipulandum fidelity on the perceived realism of drilling a virtual temporal bone. Virtual surgical contact forces rely on haptic device fidelity and are transmitted through a manipulandum. With identical software, both device hardware and manipulandum may each contribute to realism. We compare the three degrees of freedom (DOF), 3N Geomagic Touch (3D Systems, SC) to a 6DOF, 5.5N HD2 (Quanser, ON) with the both standard (“HD2–Standard”) and in‐house customized otic drill manipulandum (“HD2–Modified”). Methods Six otologic surgeons performed three virtual mastoidectomy surgeries on a temporal bone surgical simulator. The HD2 manipulandum was modified for attached otic drill with gravity compensation and requisite mechanical modifications. Surgeons, in random order, performed the dissection with the different hardware platforms. Results Two‐tailed t‐tests demonstrate that for the acoustic properties of each simulation, the HD2–Modified manipulandum was favored (p ≤ 0.0004). For overall similarity of bone, both HD2–Standard (p ≤ 0.05) HD2–Modified (p ≤ 0.03)) were favored over the Geomagic; however they were not appreciably different when directly compared to each other. There was no preference for increasing haptic device fidelity in virtual drill bone interaction. In forced rank, users favored the HD2–Modified in osseus, vibrational and overall realism, as well as being preferred for education and preoperative rehearsal (p ≤ 0.0164). Conclusion Increasing manipulandum realism was favored. However surprisingly, there was no preference for increased device fidelity, illustrating incremental stiffness had nominal impact. There may be a ceiling to drill bone interaction in virtual haptic simulation. Level of Evidence 2b.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".