Effect of thickness of <scp>CAD</scp>/<scp>CAM</scp> materials on light transmission and resin cement polymerization using a blue light‐emitting diode light‐curing unit
Bibliographic record
Abstract
Abstract Objective Evaluate the effect of thickness of high‐translucency (HT) CAD/CAM materials on irradiance and beam profile from a blue light‐emitting diode light‐curing unit (LCU) and on the degree of conversion (DC) and maximum polymerization rate (Rpmax) of a light‐cured resin cement (LCC). Material and methods The direct output from the LCU, the light transmission and irradiance ratio (IR) through one conventional composite and nine HT CAD/CAM materials (0.5, 1.0, 1.5, or 2.0‐mm thick; n = 5) were measured with a integrating sphere coupled to a spectrometer. The light beam was assessed with a beam profiler camera. The DC at 600 s and the Rpmax of one LCC was determined using a Fourier transform infrared spectrometer (n = 5). Data were analyzed by ANOVA followed by Tukey's tests, and Dunnett's test was also used for irradiance data (α = 0.05). Results A significant decrease in irradiance through all materials occurred as thickness increased. Thin CAD/CAM materials improved light homogeneity, which decreased with the increase in thickness. The DC of the LCC directly exposed to light was the same as when exposed to 45%, 25%, 15%, or 5% IRs. Rpmax decreased with the decrease in IR. Conclusions Although the HT CAD/CAM materials reduced the irradiance from the LCU, minor effects were observed in the LCC's DC. Clinical significance Despite the light attenuation of blue light through different CAD/CAM materials that were up to 2‐mm thick, the degree of conversion of one brand of light‐cured resin cement was clinically acceptable when the LCU was used for 30 s.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".