Irradiance and Radiant Exposures Delivered by LED Light-Curing Units Used by a Left and Right-Handed Operator
Bibliographic record
Abstract
The combination of the restoration location, the hand preference of the operator using the light-curing unit (LCU), and the design of the LCU all can have an impact on the amount of the light delivered to the restoration. To evaluate the effect of left-handed or right-handed users, the position of the operator (dentist or assistant), and the LCU design on the irradiance, radiant exposure and emission spectrum delivered to the same posterior tooth. Two light emitting diode (LED) LCUs were tested: an angulated monowave LCU Radii-Cal (SDI, Victoria, Australia) and a straight aligned multi-peak LCU Valo Cordless (Ultradent, South Jordan, UT, USA). The irradiance values (mW/cm2), radiant exposure (J/cm2) and emission spectrum were measured using a sensor in maxillary left second molar tooth. The irradiance and radiant exposure were analyzed using three-way ANOVA followed by Tukey test (a=0.05). The emission spectra (nm) were analyzed descriptively. The interaction between LCU design, operator position, and hand preference significantly influenced the irradiance and radiant exposure (P<0.001). In all cases, Valo delivered significantly higher irradiance than Radii-Cal. The handedness and the operator position affected the irradiance and radiant exposure delivered from Valo. Operator position and access affect the irradiance and radiant exposure delivered to the maxillary left second molar. The irradiance and radiant exposure can be greater when a right-hand operator is positioned on the right side of the chair and a left-hand operator is positioned on the left side of the chair. This may result in better resin composite polymerization.
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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 | 0.001 |
| 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) | 0.005 | 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".