Evaluation of Printed 3-Dimensional Temporal Bone Models in Surgical Procedures
Bibliographic record
Abstract
Background: The current surgical training model is based primarily on cadaveric dissection; however, opportunities are limited due to small numbers of specimens. Alternatives to cadaveric dissection such as virtual reality simulations and rapid prototyped models attempt to replicate the cadaveric gold standard in order to enhance the learning process. Cadaveric comparison to virtual haptic modeling, as undertaken in Australia, demonstrated significant differences in drilling techniques based on hand motion analysis. This raises concerns that some forms of simulation may result in the development of inappropriate and maladaptive skills. Objective: To determine if there is a significant difference in drilling technique during surgical training procedures on rapid prototyped 3D temporal bone models and cadaveric specimens. Methods: Eight (8) otolaryngology residents completed a mastoidectomy on cadaveric temporal bone and printed models. Motion sensors within an electromagnetic field were used to capture drilling technique. Results: Significant differences in the drilling technique was demonstrated. An increased number of curved strokes, and longer, faster strokes were taken when drilling the printed models. It was also noted that junior residents had significantly different drilling technique when compared to the senior residents. Conclusion: Technique growth from junior to senior level residents was shown to occur. Therefore, caution must be taken when residents drill printed models because results demonstrate altered drilling technique.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".