Radiant Exitance of Old, New, and Damaged LED Light Curing Units
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: This study aimed to determine the radiant exitance of new, damaged, and 16-year-old light-curing units (LCUs) with and without infection control barriers, and before and after removal of any debris. METHODS AND MATERIALS: Old LCUs consisted of 62 SmartLite iQ2 lights (Dentsply Sirona, York, PA). New LCUs consisted of 58 SmartLite Focus (Dentsply Sirona) and 58 Valo Grand (Ultradent, South Jordan, UT, USA) LCUs. Each LCU was examined for damage and debris on its tip. A handheld radiometer (CheckUp with BlueLight Analytics app, Halifax, Nova Scotia, Canada ) was used to measure the radiant exitance using a 10-second exposure time. Measurements were made with and without infection control barriers. If debris was present, the radiant exitance was measured before and after removal of debris with and without the barriers. All measurements were repeated three times. The means of the measurements were used for statistical analyses, which consisted of paired t-tests, analysis of variance (ANOVA), and Tukey post-hoc analyses conducted with a 0.05 level of significance. RESULTS: Infection control barriers significantly reduced the radiant exitance of all LCUs, ranging from 4.35% to 6.91% depending upon the LCU and the presence of debris or damage. Clean undamaged SmartLite Focus (907 mW/cm2) and Valo Grand (Ultradent) LCUs (883 mW/cm2) with barriers had statistically higher radiant exitance than older clean undamaged SmartLite iQ2 (Dentsply Sirona) LCUs (719 mW/cm2) with barriers. All LCUs exceeded the recommended 400 mW/cm2 radiant power to cure 2 mm of Filtek Supreme Ultra shade A2 composite resin (3M ESPE, St Paul MN, USA). CONCLUSION: Infection control barriers, debris, damage, and age all significantly reduced radiant exitance of the lights.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it