Effect of using proper light-curing techniques on energy delivered to a Class 1 restoration.
Bibliographic record
Abstract
OBJECTIVES: To determine the effect of proper light-curing instruction on the radiant exposure (energy density) delivered by dentists using six dental curing lights to a posterior Class 1 restoration. METHOD AND MATERIALS: Twenty-five dentists attending a professional meeting were instructed to position a patient simulator (MARC-PS, BlueLight), as they would for a patient, and then to expose the simulated Class 1 maxillary second molar preparation for a specified amount of time. At this point, the dentists were unaware of the purpose of the experiment. Each participant used three different curing lights, and the irradiance and radiant exposure (J/cm2) delivered to the preparation was recorded. Participants were then informed of the purpose of the exercise, and given specific light-curing instructions and training using the patient simulator, after which they re-exposed the same preparation using the same curing lights. Pre- and post-instruction radiant exposure values were compared using one-way ANOVA (α = .05), and for each light among all operators using a two-tailed, paired Student's t test. RESULTS: There was a wide variation in the radiant exposure delivered by the dentists and by the six curing lights, from 2.9 to 15.4 J/cm2. Before receiving additional light-curing instruction, 68% of dentists delivered less than 10 J/cm2. The radiant exposure delivered increased significantly (P < .001) by up to 30%, as a result of training using MARC-PS. CONCLUSION: The results indicate that some of the dentists participating in the present study delivered an inadequate amount of radiant exposure before instruction. More energy was delivered after a short training session using the MARC-PS. Reinforcing the proper photo-curing techniques may improve the outcome when placing resin-based restorations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".