Effect of Composite Insertion Technique on Cuspal Deflection Using an In Vitro Simulation Model
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to investigate, by simulation, the effect of conventional composite resin insertion techniques on cuspal deflection using bonded typodont artificial teeth. The deflection produced by a new low-shrinkage composite was also determined. MATERIALS AND METHODS: Sixty standardized MOD preparations on ivorine maxillary premolars were prepared: group A at 4 mm depth and group B at 6 mm depth. Each group was further subdivided according to composite insertion technique (n=6), as follows: 1) bulk insertion, 2) horizontal increments, 3) tangential increments, and 4) a modified tangential technique. Preparations were microetched, acid-cleaned, and bonded with adhesive resin to provide micromechanical attachment before restoration with a conventional composite (Spectrum TPH( 3 ), Dentsply). Two additional subgroups at 4 mm and 6 mm depth (n=6) were restored in bulk using low-shrinkage composite (Filtek LS, 3M/ESPE). All groups received the same total photo-polymerization time. Cuspal deflection was measured during the restorative procedure using two Linear Variable Differential Transformers attached to a data acquisition system. RESULTS: The average cuspal deflections for group A were 1) 40.17 ± 1.18 μm, 2) 25.80 ± 4.98 μm, 3) 28.27 ± 5.12 μm, and 4) 27.33 ± 2.42 μm. The deflections in group B were 1) 38.82 ± 3.64 μm, 2) 50.39 ± 9.17 μm, 3) 55.62 ± 8.16 μm, and 4) 49.61 ± 8.01 μm. Cuspal flexure for the low-shrinkage composite was 11.14 ± 1.67 μm (group A: 4 mm depth) and 16.53 ± 2.79 μm (group B: 6 mm depth). CONCLUSIONS: All insertion techniques using conventional composite caused cuspal deformation. In general, deeper preparations showed increased cuspal deflection-except in the case of bulk insertion, which was likely affected by decreased depth of cure. Cuspal movement using low-shrinkage composite was significantly reduced.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".