Effectiveness of Using a Patient Simulator with Real-Time Feedback to Improve Light-Curing Skills of Dental Students
Bibliographic record
Abstract
The present study investigated the effectiveness of employing a patient simulator with an integrated visual feedback mechanism to improve the light-curing skills of dental students. A total of 44 third-year dental students were randomly divided into a control group (n = 22) and a training group (n = 22). Both groups light-cured a simulated restoration in tooth 27 in a patient simulator (MARC Patient Simulator, BlueLight Analytics, Halifax, Canada) by using the same curing device for 10 s. Delivered irradiances were recorded in real time by the built-in spectrophotometer. After measuring the baseline irradiances for both groups, the training group received detailed light-curing instructions and hands-on training with immediate visual feedback using the patient simulator. The irradiance of the training group was re-measured after the training. Both groups then attended a 26-day preclinical course, which involved placing 30 composite restorations. Upon completion of this course, the light-curing performance of both groups was re-assessed. The data were statistically analyzed using the Wilcoxon signed-rank test, Friedman’s ANOVA, and the Mann-Whitney U-test at an overall level of significance of α = 0.05. At baseline, the control and the training group delivered statistically similar irradiances with similar data scattering. In the training group, data scattering was considerably reduced after the hands-on training with the patient simulator. After the 26-day preclinical course, the irradiance of the training group was significantly higher and considerably less scattered compared to the control group. In conclusion, training with the patient simulator improved the light-curing performance of the dental students, mainly by helping them to deliver light energy more consistently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".