Radiant Exitance of Old, New, and Damaged LED Light Curing Units
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".