Effect of Contamination, Damage and Barriers on the Light Output of Light-Curing Units
Bibliographic record
Abstract
Background: Light-curing is a crucial step during the application of composite resin restorations. The clinical success of composite depends on the Light-Curing Units (LCU) to deliver adequate light energy to polymerize the resin. However, light-curing usually does not receive the proper awareness it deserves. Objective: This study aims to evaluate the effect of contamination and debris of the LCU’s tip on its light output. Determine the effect of damage to the LCU’s tip such as chipping, dents and scratches. Additionally, it evaluates the effect of plastic barrier sleeves. Methods: Sixty LED LCUs were tested using MARC™ Resin Calibrator (BlueLight Analytic Inc., Halifax, Canada) to measure their irradiance and energy before and after cleaning their tips. They were also tested with and without a clear plastic barrier. Additionally, four damaged LCUs received new tips and were tested again. Kruskal-Wallis H and One-Way ANOVA tests were used for statistical analysis. Results: Cleaning the LCUs’ tips showed significant improvement, an average increase of 8.2%. However, some units increased by up to 47% in irradiance and energy values. Replacing the damaged tip with a new one significantly improved the output of the LCUs, increasing light energy by up to 73%. The barrier used in this study caused 7% reduction in the energy delivered by the LCUs. The statistical analysis showed that cleaning the LCUs and replacing their damaged tips resulted in a significant increase in energy ( p <0.05). Conclusion: Unclean or damaged LCUs’ tips can drastically reduce the light output of the LCUs, reducing the quality of the composite restorations. Clinicians are strongly recommended to regularly monitor, clean and maintain their curing lights.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".