Performance of Dental Students Versus Prosthodontics Residents on a 3D Immersive Haptic Simulator
Bibliographic record
Abstract
This study evaluated the performance of dental students versus prosthodontics residents on a simulated caries removal exercise using a newly designed, 3D immersive haptic simulator. The intent of this study was to provide an initial assessment of the simulator's construct validity, which in the context of this experiment was defined as its ability to detect a statistically significant performance difference between novice dental students (n=12) and experienced prosthodontics residents (n=14). Both groups received equivalent calibration training on the simulator and repeated the same caries removal exercise three times. Novice and experienced subjects' average performance differed significantly on the caries removal exercise with respect to the percentage of carious lesion removed and volume of surrounding sound tooth structure removed (p<0.05). Experienced subjects removed a greater portion of the carious lesion, but also a greater volume of the surrounding tooth structure. Efficiency, defined as percentage of carious lesion removed over drilling time, improved significantly over the course of the experiment for both novice and experienced subjects (p<0.001). Within the limitations of this study, experienced subjects removed a greater portion of carious lesion on a 3D immersive haptic simulator. These results are a first step in establishing the validity of this device.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".