Development and face validity testing of a three-dimensional myringotomy simulator with haptic feedback.
Bibliographic record
Abstract
INTRODUCTION: Considerable progress has been made in the development of three-dimensional temporal bone virtual reality simulators. However, very little attention has been paid to interactive simulation of middle ear surgery, including myringotomy. As the development of technical skills for middle ear surgery has a steep learning curve, this poses inherent risks to patients during the skills acquisition phase. The benefit of simulation training in the acquisition of surgical skills has been demonstrated with previous low-fidelity models. Tactile sensation is an important feedback mechanism in middle ear surgery, so a three-dimensional virtual reality myringotomy simulator with haptic feedback was developed at The University of Western Ontario. OBJECTIVE: To examine the face validity of a three-dimensional myringotomy simulator with haptic feedback. METHODS: The three-dimensional myringotomy simulator was calibrated with input from two otolaryngologists and one intermediate-level resident. The face validity of the resulting simulator was tested by four staff otolaryngologists and seven intermediate- or senior-level residents using a previously validated questionnaire. RESULTS: The results demonstrate good to excellent face validity for the myringotomy simulator with high consistency between participants with a Cronbach alpha of .919. Favourable responses predominated for all questions with the exception of force feedback, where the average response was neutral. CONCLUSIONS: The initial results from the development and testing of a three-dimensional virtual myringotomy simulator with haptic feedback are very encouraging. This simulator is the first of its kind and, with further refinement, has excellent potential to be of benefit in the training of proficient middle ear surgeons.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".