Training and experience effect on light‐curing efficiency by dental practitioners
Bibliographic record
Abstract
BACKGROUND: Light-curing is a crucial step during the application of composite resin restorations. Composite's success depends on delivering enough light energy to the resin to achieve adequate polymerization. However, dentists are not recognizing the importance of proper light-curing technique. OBJECTIVES: To measure light energy delivered to simulated restorations by preclinical dental students and dentists in internship year. To evaluate the effect of experience and training on the clinician's ability to light-cure composite restorations. METHODS: A group of 50 preclinical dental students and a group of 50 internship dentists light-cured for 10 seconds, a simulated class III and class I restorations positioned in a patient simulator (MARC-Patient Simulator [BlueLight Analytics Inc., Canada]) that measured the irradiance and energy delivered by the curing light. Then participants received individualized training on optimizing their light-curing technique. They were retested after the training. Statistical analysis was done with two-way ANOVA and Tukey's test. RESULTS: Participants delivered an average of 60% more energy after the instructions, which is a significant improvement (P < 0.05). The number of participants that failed to deliver the minimum amount of energy (6 J/cm²) decreased significantly from 37.5% to 2.5%. There was a significant difference in the amount of energy delivered by the Preclinical and Internship groups (P < 0.05). CONCLUSION: Initially, many participants were not using the curing light properly. Light-curing technique improved with training and using a patient simulator. Experience can enhance the operator's ability to light-cure composite restorations. However, a training session can improve light-curing performance more than years of experience.
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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 | 0.010 |
| 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) | 0.006 | 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".