The Ability of Dental Practitioners to Light-Cure Simulated Restorations
Bibliographic record
Abstract
OBJECTIVES: To determine the ability of dental professionals to deliver a radiant exposure of at least six J/cm2 in 10 seconds to simulated restorations. METHODS AND MATERIALS: The study initially examined 113 light-emitting-diode (LED) light polymerization units (LPUs) used in dental offices to determine if they could deliver at least 6 J/cm2 radiant exposure (RE) in 10s. This assessment was completed by using a laboratory-grade light measuring device (checkMARC, BlueLight Analytics, Halifax, NS, Canada). The participating dental professionals whose LPUs could deliver 6 J/cm2 then used their own LPU to light-cure simulated anterior and posterior restorations in the MARC Patient Simulator (BlueLight Analytics). They then received specific instructions and were retested using the same LPUs. Data were statistically analyzed with a series of one-way analysis of variance (ANOVA), two-way ANOVA, paired-samples t-tests, Fisher post hoc multiple comparison tests, and McNemar tests with a preset alpha of 0.05 (SPSS Inc). RESULTS: Ten (8.8%) LPUs could not deliver the required RE to the checkMARC in 10s and were eliminated from the study. For the anterior restoration, most dental practitioners (87.3%) could deliver at least 6 J/cm2 before instructions. After receiving additional light-curing instructions, only two (1.9%) participants were unable to deliver 6 J/cm2 to the anterior location. At the posterior location, only 55.3% (57) participants could deliver at least 6 J/cm2 before the instructions. After receiving these instructions, an additional 32 participants delivered at least 6 J/cm2. Overall, after receiving instructions on how to use the LPU correctly, the participants improved the amount of RE they delivered to anterior and posterior restorations by 22.5% and 30%, respectively. CONCLUSION: This study revealed that at the baseline, 44.7% of participating dental professionals failed to deliver 6 J/cm2 in 10s to the posterior simulated restoration when using their own LPU.
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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 | 0.008 | 0.054 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".