An Evaluation of the Efficacy of LED Light Curing Units in Primary and Secondary Dental Settings in the United Kingdom
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the irradiance and the quality of LED light curing units (LCUs) in primary and secondary clinics in the UK and to assess the effect of damage, contamination, use of protective sleeves, and distance of light tips to target on the irradiance and performance of LCUs. METHODS: The irradiance levels (mW/cm2) of 26 LED LCUs from general dental practices and 207 LED LCUs from two dental hospitals were measured using a digital radiometer (Blue Phase II, Ivoclar, Vivadent, Amherst, NY). Ten LED light guide tips (Satelec Mini, Acteon, Merignac, France) were selected to evaluate the effect of chipping, contamination (tip debris), and use of protective sleeves and tips to sensor distance on irradiance (mW/cm2) using a MARC Resin Calibrator (Blue Light Analytics, Halifax, Canada). Homogeneity of the light output was evaluated using a laser beam profiler (SP620; Ophir-Spiricon, North Longan, UT, USA). Statistical analysis was conducted using a one-way analysis of variance (ANOVA) with post hoc Tukey test (α=0.05) and linear regression with stepwise correlation tests. RESULTS: Thirty-three percent of the LCUs delivered irradiance output less than 500 mW/cm2. The condition of the light curing tips was poor, with 16% contaminated with resin debris, 26% damaged, and 10% both contaminated and damaged. The irradiance output was significantly reduced in contaminated (62%) and chipped (50%) light curing tips and when using protective sleeves (24%) (p<0.05). Irradiance was also reduced when increasing the distance with 25% and 34% reduction at 7 mm and 10 mm, respectively (p<0.05). CONCLUSION: There remains a lack of awareness of the need for regular monitoring and maintenance of dental LCUs. Damaged and contaminated light curing tips, use of protective sleeves, and increasing the distance from the restoration significantly reduced the irradiance output and the performance of the LCUs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".