Depth of cure of 10 resin-based composites light-activated using a laser diode, multi-peak, and single-peak light-emitting diode curing lights
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Bibliographic record
Abstract
OBJECTIVES: To evaluate the depth of cure (DOC) of ten contemporary resin-based composites (RBCs), light-cured using different LCUs and exposure times. METHODS: The power, radiant emittance, irradiance, radiant exposure (RE), and beam profiles from a laser (M, Monet), a multi-peak (V, Valo Grand), and single-peak (S, SmartLite Pro) LCU were measured. The DOC was measured using a 6-mm diameter metal mold and a solvent dissolution method to remove the uncured RBC. The length of the remaining RBC was divided by 2. The exposure times were: 1 s and 3 s for M, 10 s and 20 s for V, and 10 s and 20 s for S. Data were analyzed using: Bland-Altman distribution, Pearson's Correlation, and an artificial neural network (ANN) to establish the relative importance of the factors on the DOC (α=0.05; β=0.2). RESULTS: Significant differences were found in the DOC achieved by the different LCUs and composites. The laser LCU emitted the highest power, radiant emittance is used above and in the tables and delivered the highest irradiance. However, this LCU used for 1 s delivered the lowest RE and produced the shortest DOC in all ten RBCs. The ANN demonstrated that the RE is the most critical factor for the DOC. Bland-Altman comparisons showed that the DOCs achieved with the laser LCU used for 1 s were between 17 and 34% shorter than the other conditions. CONCLUSIONS: Although the laser LCU cured all 10 RBCs when used for 1 s, it produced the shallowest DOC, and some RBCs did not achieve their minimum DOC threshold. The RE and not the irradiance was the most important factor in determining the DOC of these 10 RBCs. CLINICAL SIGNIFICANCE: Despite delivering high power and irradiance, the laser used for l s delivered a lower radiant exposure than the conventional LCUs used for 10 s. This resulted in a shorter DOC.
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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) | 0.001 | 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 it