Micro-computed tomographic assessmentof the influence of light-curing modes oninternal void formation in bulk-fill composites
Bibliographic record
Abstract
Introduction: Polymerization reactions in a new generation bulk-fill composites carried out in a short time with high irradiation, raise concerns about curing processes. With micro-computed tomographic evaluation, it is possible to investigate polymerization shrinkage, and subsequent gap and void formation in dental materials. Objectives: The aim of this study was to evaluate the void formation in bulk-fill composites light-cured with different modes using micro-computed tomography. Material and methods: Class I preparations were made in 25 molars that were randomly divided into subgroups, according to resin composite and curing mode used: Tetric EvoCeram (TEC)*high power mode, TEC*turbo mode, Tetric PowerFill (TPF)*high power mode, TPF*turbo mode, and TPF*3s mode. Each tooth was scanned at two time intervals: pre- and post-cure. Results: After light-curing, a significant increase in the total volume of internal void was noted for both composites cured with high power mode compared with pre-cure. The difference between the sub-groups at post-cure was also significant. While TEC exhibited similar values in terms of different curing modes, turbo and 3s modes caused a significant difference in TPF group, and the lowest void percentage was detected in 3s mode. Conclusions: Internal void formation results from an interplay of different factors, including composition of materials and curing modes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".