Effect of using curing light manufacturer-recommended exposure times on the microhardness of resin composites
Bibliographic record
Abstract
OBJECTIVES: This study evaluated the effect of the light curing unit (LCU), light curing technique (LCT), and insertion technique (CIT) of resin-based composite (RBC) on the microhardness at various depths of RBC specimens. METHODS: A mesio-occlusal-distal (MOD) mold was used to make samples of conventional RBC (n = 3/group) using either an incremental (I) or a bulk-fill (BF) technique. The RBC was photocured using one of three different LCUs (Bluephase Style 20i for 15 s (I) and 10 s (BF), Monet laser for 1 s (I) and 3 s (BF), and Pinkwave for 10 s (I) and 20 s (BF). The LCUs were positioned either only over the center of the mold, or at three sites (side-center-side). The Vickers microhardness was measured at different distances from the top of the sample. Four-way ANOVA and Pareto chart analysis were performed (α=0.05). RESULTS: All the experimental factors were significant (p ≤ 0.05). The Pinkwave produced the highest microhardness, followed by the Bluephase and Monet. Light exposure from the three sites produced higher microhardness than light exposure only at the center of the mold. The incremental technique resulted in higher microhardness than the bulk-fill technique. The depth of the RBC negatively affected the microhardness. CONCLUSION: RBC microhardness varied depending on the type of LCU used. The Pinkwave delivered the most energy and produced the highest microhardness values. CLINICAL SIGNIFICANCE: To ensure uniform microhardness of the RBC, the tip of the LCU should cover all aspects of the restoration (occlusal, mesial, and distal surfaces).
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".